{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 你的第一个神经网络\n",
    "\n",
    "在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 加载和准备数据\n",
    "\n",
    "构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 数据简介\n",
    "\n",
    "此数据集包含的是从 2011 年 1 月 1 日到 2012 年 12 月 31 日期间每天每小时的骑车人数。骑车用户分成临时用户和注册用户，cnt 列是骑车用户数汇总列。你可以在上方看到前几行数据。\n",
    "\n",
    "下图展示的是数据集中前 10 天左右的骑车人数（某些天不一定是 24 个条目，所以不是精确的 10 天）。你可以在这里看到每小时租金。这些数据很复杂！周末的骑行人数少些，工作日上下班期间是骑行高峰期。我们还可以从上方的数据中看到温度、湿度和风速信息，所有这些信息都会影响骑行人数。你需要用你的模型展示所有这些数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bd990d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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znnQ08RbFhQo+FXxCiB3bCvQspBh0RQghQbBbdPJJ0fn6XadGt+9evARfs55dRAW/z0FX\nhBALtWqyJYSQmOSu4Kvbt4jqtfn+LGKKjnoRSgWfEFLAAp8QQhzYFPzNTi+bYrq74PYMU7FeRA+6\nek3T6UlYWssIIQsIC3xCCHHgOkDm0mirDzlavMKOk2zZh0AIsVO7QVeEEBILl+MjF5uOVuAvoj1l\nwVN0pJQL/xoQQuz4PhawwCeEzA2uFIJcCnzdorN4hd2iq9e2p7uIVi1CyDi2HrJZYIFPCJkb6mXR\nWbzCbjwHf7FeA9v+uYgXeoSQcRiTSQghDlwexvVMojK1BJWA6vWdJ9bxqZsezE4dXvQmW9v+uYgX\neoSQcXwfD0NOsiWEkKi4DpDntjqRt8ROrxdewT+1vo3n/eHnsNXt43U/eSVe95OPC/I4u2HRJ9la\nFfwFew0IIXbYZEsIIQ5cB8hchl11I6TofPPeM9jq7lw8fOF7x4M8xm5ZdA++bQmeCj4hBGBMJiGE\nOHEdIHNpsu1HsOiow6NObWwHeYzdsugJMrYmOnrwCSEAC3xCCHHialLKpcm2G6HJVl0ZOL2RhzWp\nYDwHf7HUa9sFTW59EoTkwNeOncQr//pr+H9vuCf1pkTDd5MtPfiEkLnBFTOWi4KvFrShLDqqCnR6\nswMpJYQQQR5rWsznTAV/8RqNCanCf/rYjfjmPWfwuVsexk898ZHYv9JOvUnBoYJPCCEOco/JVIu5\nTiD1Wi2ae32JtUyeO8BJtjaFLsR+sLndwy/+5Vdw9ds+h1sfOuf9/gkJzf1nzgMAtrr97FYiQ8EC\nnxBCHORu0VEL3BgKPgCcXs/n5DjmwV8w9dp2Ag/xGvz3mx7EF249ju8+sIa/+cpd3u+fkND0DaFi\nEWCBTwghDlxiaC4WHX2SbXgFH8ir0dasZRflxF1g2z9D9GKc3Rxd1J3ezOf9J6QqWr/SghwnGJNJ\nCCEOVAVftZ1nM+gqwknLbFzNqcBf+Bx8ywl8O0CB39MuJBfrNSbzQV87Vi5GI7rv4yELfELI3KCe\nFPYtjzIEcrHo9GKk6JgWnYz8q+a2LVqKTiyLjrZS1F2s15jMBzFmhuREvy/hWcBngU8ImR/UAuqA\nkrqQS4EfY9l5zINPBT8brAV+gIsc9cKJMZykjqirXYtg5fMdkQmwwCeEzBHqQfLgnlGBn4sHvxdB\nlTJPhqcyUvAXfpJtpEFX6oVTCAsQIaGJYWfMiRDHQhb4hJC5QVWID+zJ3KITyJ5SKwV/AZbeVWxN\ndCEU9p7yum7TokNqhpRSO44tghDgu8EWYIFPCJkjVIVYHYyyvtWFDHAAnZYYzY/jKTr5KviL0jxX\nkMSDTwWf1AzzYxKqXyknQqxSsMAnhMwNagG10m5iqbVziOtL4Hwn/UlCbxyLo+DnlKJjbltfuqcP\nzyOxBl0xRYfUGfPCfxEsOiGOgyzwCSFzg1rYNEV+STpa82Ogk5apCOeUomNTsEM0l+WK7SROBZ8Q\nHfOadxEsOvTgE0JICepBstEQWGqODnE5FDoxYjJzzsG3FvgLcPIusDfZhk3RoQef1I1FVPBZ4BNC\nSAlqo1JTCLRbo2lXuRX4oewp5snwTEYKvq2RbBFO3gVM0SFkMqaCvwgefMZkEkJICep5oNkQaDdU\nBT99IWkWs6H91wCwttXN4uIGcCj4GbwvsbCdxEMULz1adEiNoYLvBxb4hJBonN7Yxgeuvwf3n9kM\ncv9qAdVoCLQzs+jEiIm0nQxz8eHHGvSUK1YFP/AqTg4XtoRMwyLOywjxHFuTf4UQQvzw+vf9Mz5z\n88O4/MK9+PQbno1GQ0z+oylQC+gcLTpm8R2iwLedKE5vbOOi/cveH2taFt2Db7UoBc7B79CDT2qG\neUyggr87qOATQqJx/Z2nAADHTmzgxLr/5k8tRach0MrMomMexENYdGyKeC5Z+FaLygKcvAtstXyI\n56/e51YGF7aETIMpfJjBAb44tb6Nt/zDd/G3X7s7yP1PQ4hBV1TwCSHR0HLgAxy01YNkQ2SYomMO\neoqk4OeSpGN7yxdJwbc91xApN1oca68PKSWE8LtaRkgozGI3lDjzzs/dhj//7O0AgMcfPYAfevTB\nII9TBQ66IoTUGn3QU9jittlAdhYds6E0TETi+OuaS5LOoiv49hShEKs4o8eRcrEuokj9MY8Jofbf\nOx5eH97+7gNngzxGVWjRIYTUGtVvHKS4NZpsVYtOiAuKaRnz4EeIyQTyUPCllA4PfvoLr1jY3psY\nqzg52NMIqcpYGEGgAl/9nJzZTCuChDgMssAnhESh35dQj9MhDtpjTbaKRSeHPPBxi04cBT8HD77r\n7V6k4tM29yB0Dj6Qx75PSFXGFfww+6/6OGcTF/ghVvJY4BNCojCWAR+kuB3dbjYElnKz6ERQVu0x\nmekVfNcS9CLZR2LFhI7vZ+n3fUKqEitFR32c06kVfA66IoTUlbHhJSGsCUaTbU4WHZtFJURxaxsc\nlYNFx3UCWyQPvq0HIUTxPabgMyqT1IixAj/QsTsni06I58gCnxASBVOtDpKiY8Rk5mTRsQ85Cl/c\nAXlYdNwK/uIUn7EsOuZrSgWf1IkYvUpAXgW+7eJ/VljgE0KiEMOeoh4kc7PoWBNkIhR3QB4pOq4T\nWOqVlZjY94EAF3kR0poICYW52hfOgz+639QFPptsCSG1xSxkwsdk5mXRsfqvI9gzgEwsOvTgW1+D\nIM3mRoG03V2c15jUH/NYvQgKPptsCSG1pRNhiqtW4BspOqlVTNtJynxNfGArmE9vdCADLAFPg+sk\nvUgefOs+EOEiL/W+T8g0jCn4gcQZ9XOSepWTTbaEkNpiHqRDK/iNhtAGXaX24FvV20gK/navj43t\nnvfHmoaYCv637z2DV7/nerz3uru83/csWPswIuTgp973CZmGVB78lCJIiI8oC3xCSBRMxT5Ecauq\nIE0BtDOy6NjV2zgKPpDepuP04Ac4ef/ex2/CJ779AH7zQ9/CPac2vN//brFOso3hwWeKDqkR44Ou\nwuy/6rGy25dJRZAQfQYs8AkhURgrOgLbU8wUndQ2hVQZ6AWnEy9Bx0zReeDMeQA7w7W+dc8Z7/e/\nW2y7IBV8QnTGB12FV/CBtD58KviEkNoynoMfVsE3LTqpJ6bam2zDFnf7llvD26kVfFcdH0LBV1eL\nbn5wzfv97xargh8kKtWMyVycPgdSf2Ll4JvHnqQFPj34hJC6MpaMEDpFRwjNopOjgh+6wfLCfUvD\n26kVfFchG0KdU/etWzIq8FN58FPv+4RMQ4yBgLb7TXmMpEWHEFJbxlTFIPaU0e1GQ6DdzCcH36ZU\nh1Cv1RPFkX3Lw9unUyv4EXPw1df15gdyL/A5yZYQlfFzBS06u4EFPiEkCjEUfL3JVqClefBTW3TG\nj+ChU3SOKAp+6mm2rqcaxKKiPNixExs430mbIFSQwqYF0INP6kW8QVf645xNWOC7UsZmgQU+ISQK\nMbK5zSbbpayabMe/F9qecXh1pOCnPHkB7mX2EKsY6n32+hK3P7zu/TF2g3WSbRAPPi06pL7EEIOA\n8QuHlAp+iOMgC3xCSBTMIiOIPaW0yTa1Rcei4AdRr0evwf6VUZPtZmIV22XRCe3BB4DvPZSHTcem\n0kXx4NOiQ2rEuILPJtvd0Jr8K4QQMjvjyQgBUnSMJluRUQ5+igZLNUVnM/GgK+ck2yAefH3fysWH\nH82Db9xnansaIdOQYtAVAJzeTNenFMKiwwKfEBIFs8gIXdw2G0BTWaRM7UOO5b9WT4Y5KfjuHPyw\nrwGQT5KO1aJDDz4hGikGXQHAmc1ukMepQoiLGBb4hJAojCn4AQ7aWpNtowHFgp/cohNv0NXoPvev\ntIe3U05pBEpSdDyf2Hp9CfOhcsnCt1p0InjwmaJD6sSYgh/Mg5+PRYdNtoSQ2jI+6Cq8gt/KyKJj\nK2RDrGJ0M7XoxJpka7touvvkJta30qlzBTYFX0r/qxjMwSd1JkYOvpRy7j34LPAJIVGIYdFRD9gN\nIdBu5mPRsSk0IeLf1JPhgYwsOi6FyreC77qQ+95D57w+zm5wFSo+C3Bb4cICn9SJ8dXeEJHK499L\nmTQW4iKGBT4hJApmMRveoiOwlFWKTvwmW92ik1bBdilUvk9srgL/lgwabWMU+LaHYJMtqRPjTbYh\nbGzj95lyGCALfEJIbYneZCtEVhadeB58xaKzko9Fx5mi47vAd7ymOfjwncO+PO6btuefevWKkGkY\na7INMRTR8pE4e74LGcAqUwXm4BNCaosZ3RcmJnN0u9HQLTqpFfwYKTqmPSOnFB2XRce7gu+4vxyS\ndFyNxj4bbW2vJ5tsSZ0wP8NhkrbGPxO9vsS5RL06bLIlhNSWGNnGvRKLTmoVM4ZFR32IhgBWl0YF\nfuoUHeckW8+vgfo6i9Hbn0UWfozXwL6fscAn9SHGoCvXfaZqtGWTLSGktsRo/FMP2o0FtOio99dq\nNLDcGj3/rW4/2ETIKrgn2Xp+DZT96uIDK8PbJ9bT+WsLXCdxn/tmz3JfLPBJnYghBrnuM1mBTwWf\nEFJXxi06IZIRdAW/3crHomMr5n2/BnpMqECjIbCn3Rx+L6VNx/Xydzyf2NRVkZV2Ew1RPL4MYgub\nBtcyvM/VJVvhst1lky2pD+Me/LBikMqZDRb4hBAyFTGSEcwm23ZTTdFJW+TYFGzfFx1do8AHgL1L\nSoGf0Kbjer9tivMsmBc5y63R88/RprXzfZ8pOlTwSb2JYuekgk8IIX4w1erQKTqNBtBuZKTgW56v\n9ymuvfECf6WdR4Efa5Kt+j63mg0stxWbUidPBT+0B59NtqROxBh0xQKfEEI8YSaFxMjBz8miYzuA\nh1TwWxYFf6OTLgtffapq86tvD37PeA3MPoSUuDz4PvcDevBJ3Ykx6Co7Dz6bbAkhdcUsPOLk4I8q\nyeRNtpYDeGgPPpCPRUdVr5eU+NKQOfitpsCSUuCnVrLdg67C5uCzwCd1IkZMpktYSFXgMyaTEFJb\nxnyVIXLw1ZhIIwd/u9dPNsQESJGis1Pg78mkwFcvcNSi2/fJWy2W242G5sHf6iaeBeBM0Qmcg89J\ntqRGmMVuiAtUl7BwOlGBz0FXhJDaYh6kQzdONYVAsyGGSnaox6yKTa33vYqhPf9Bg7GaopMyC199\n7dWi23sfwliTbUYWHYuFCvCbJMQcfFJ34ij4eVl0qOATQmqLeUANbtEZFFC52HRs6q1vBV8vIHcO\n73uX8phm29cK/JAKvm7R0Qv81NN8R7fV7Qqt4LPAJ3XCPFZ2+9L76qvruHN2jjz4rcm/QgipE9+4\n+zQ++o37hif1x1y4ihdfdSlWl9N+3M2CPoxFZ7zAX2o2hsrtdq+PPWha/zY0NmU1hgc/G4uO6sFv\nhfPgmyr5UkYKvnpBt9JuYn3wfvj14DNFh9Qb2z7cl4CSeuz1MRpiZO9MpeCHWF1mgU/IHLGx3cVL\n/+orOHteT0s5t9XFr/3ElYm2agdTrQ5u0RkUuO1WA9ja+V5KJTNVio5u0UmXoqNefKlNtr5TdNRi\nudVsYFl52VMX+Godvxwo4cn2elLBJ3XCtg93+300G/7EGfV4fGjv0nDSNS06hJAsue/05lhxDwA3\n3nc2wdbomAV9iKJDy8EXeVl0ouTgT0rRSZgD71TwPb8n6oVk27ToZJSDv9xW+xA8TrK1vJ5U8Emd\nsAYS+D5OKPd3aHVpePv0HE2ypYJPyBzhWupP7T0Gxi05IYrtnsWioybpJFXwI8RkWhV8zaKTTsHv\nRvLg6xc5DShvf/LPQc/xGvi06NhXipiiQ+qDPXHM7z6srigeVgr8s+c76PclGg2PfqAKcNAVIaQU\nV8F4PrFyCYxvW4hBV2aKDqCrxdtJLTr2ZedQj9GwWnQyabJth/Pg6zGZQrMDpVay1aJCU/ADe/Bp\n0SF1wlbs+i6ATcFh36BHTUpgbSu+EMJBV4TUnF5fBm10NKfFFqRWLgGbRcf/AU0tIgchMvlYdCIv\nO9sm2aZM0ek5PPghL3KaDaFdTCT34Cv7wEorzGvgUj9DeHwJCYH1WBnwONFqCBxYGRlaUiTpUMEn\npMasne/guf/lWlz15k/iS7edCPIYWgGhFDZZKPhmk20ID37GFh1bgeV7e+wpOkpMZiYKfkgP/liT\nbUYpOj27VdHPAAAgAElEQVSHgu9zZcG1IpJy9YqQabBFCntX8Hv6sXKlrQ7Ei/9ZYYFPSI259uaH\nceeJDZzb6uKDN9wT5DHUgnGfEouZhYJvFHI+h/sUqNcQRZNtOxOLjl2VCunB33neuVh09Em2o23y\nf+I2m2zzmWTr8uD73A9cqUS06ZC6YA0kCBwpvBQo1Wo32+OLoAW+EOJFQog/EUJ8XghxVgghhRDv\ncfzu5YOfu/69t+RxXiaEuE4IcU4IcUYIca0Q4mfCPTNCpkdVT0MpqepBcFUr8NOf3M0iJpqCn4lF\nJ0YyxOQUnZQ5+KPbIZtsuyUn7tQefGeB7/Gz4LpYYKMtqQv2oYCej5VSF0PaiXt16pii80YAPwzg\nHIB7ADy+wt98A8CHLd//tu2XhRBvBfCGwf2/C8ASgJcA+KgQ4rVSyj/dxXYT4h31ABVKSVZtMKqC\nn4VFx3jOfQnvaQW2JttcLDrWdBPPvtLJKTopC/zRcw056Eq9v3ZmFh3VpqRaAkKn6Ow8RvpjACFV\nsB0TfM/LyE7Br+Ek29djp/C+FcCPA/hMhb/5Zynlm6rcuRDiWdgp7m8DcJWU8tTg+28BcD2Atwoh\nPialPDb9phPiF7X4DnUA6TgV/AwsOo4Cd9nT8BLT495ojFt0civwpdz5ftPTRY7ZYAroCv5GJ11M\npvrS64Ouwq5i5JSDr57EV9phmmxdq0KpVy8IqUqMmEwzkKCtjMlN8Vmp3aArKeVnpJTfkzLApckO\nrxp8fXNR3A8e9xiAPwOwDOAVgR6bkKlQDyihCk31MTQPfhYKfliLis2eA+gWnZQ2Bbd1wmeCyuh2\nqzkek5m0yVZtMA2UIAPor2e7IbRm1tQXurpNKa6CzyZbUhdi2xkbDaH1BaX4rISY7J5jk+2jhBD/\nuxDiNwdfn1zyu88dfP17y88+YfwOIUlRDyidbphCUy2WVOV2u9dPHpNns6N4LfAt9hwgH4uO6/X3\neWDvagr+oMk2G4uOPUUnZDpGq9nAcjMji47jIsfnfhnjQpKQkERR8A07Y+p5GXX04O+Gnxr8GyKE\nuBbAy6SUdynfWwVwCYBzUsr7LffzvcHXxwXaTkKmQi1wQykEqhK41GxgqdUYHqy2e32seLLD7IbQ\nHnS1eGoo0kUuFh3XCcpng2XPOGkBwF4lJnMjyxz8sE22ag5+apuK3mQbZtCVM0UnkKhAiG/sg648\ne/CNFd+llmLRSXCesDUWz0pOBf4GgN/FToPt7YPvPRnAmwA8B8CnhBBPkVKuD352cPD1jOP+iu9f\nUOXBhRDXO35UpTGYkIn0Ilh0tOEdzR3/cVHUnO/0tMa+2AS36FgiIoF8LDru5kefCr4lBz+XmEzl\neapFd8/zezIek6kq+KktOvbXwKsHnxYdUnOiWHSUz4Op4KcQgubaoiOlfEhK+R+klDdIKU8P/n0O\nwPMAfAXA9wP4pbRbScjuUXPfYzTZtpqN5MM7VGzP2efroGfgj27nYtFxFXGhppgWCv5Ku4HCsbTd\n7QdZCq6CNuRJVa+DKvgNIwc/JwVf3S+ZokNIgS1RJmycbvqYzNo12fpAStkF8JeD//6Y8qNCoT8I\nO8X3T1d8nKfb/gH47tQbTYiFnpaiE8iDbzYYKkXE+YT2DCC8r9LVZNtqqjn4KVN07N/3qUypr2eR\nIiSE0BttE+0Hrkm2/k/cuoK/lG2KjtpkG+YiTyW1PYmQqlhXe4OmbenHpO0EK70hYjKzL/AHPDz4\nulp8Y2DVuRfAPiHExZa/uXLw9ZbA20ZIJbQc/EAnW61xqAYKvt8BP+MRkYCu4Kc4cBfEmDBqLjsX\n6DadNFGZbg++51kAWvydnoOf2qai5+Crg67CXOSpUMEndcEuBoWbGZKDgu/bqgjUp8B/xuDr7cb3\nPz34erXlb55v/A4hSYkRk6lbdPJS8K05+B4ParpFZ1Tcph5gUuBssvWaomNfxVCTdM5vp3kNNPtQ\nUwxtQ8XAM1+Y6Ri6RSfxKpbDphRDwWeBT+qCTc0OGZPZMla7kxT486zgCyGeJoQY2x4hxE9gZ2AW\nALzH+PE7B19/SwhxSPmbywG8BsAWgHd731hCdoEWkxksB1+16OSl4NsKWa/+c5dFp5GHRceVkhCq\nuFOfdw7DrswmaO198Vngq6sYTT1FJ6VFR0oJdRcIlSTkbrJlig6pB/YUnZAWnQwm2QZ4yKApOkKI\nFwJ44eC/RwdfnymEuGZw+7iU8jcGt/8AwJVCiC9iZ/otsJOiU+TY/7aU8ovq/UspvyiE+AMAvw7g\nm0KIDwBYAvBiAIcBvJZTbEkuqAcN32rE8DEynuJpK679KviK/9yRg5+yyHG95z5PXOayc0EOSTqm\n57XZEMP3P9xroKdjpLzI1QbriHDxre6YTCr4pB7EyME3xRCJtJNsfceAAuFjMp8C4GXG964Y/AOA\nOwEUBf7/A+B/BnAVduw1bQAPAng/gD+VUn7e9gBSyjcIIb6FHcX+lwH0AdwA4C1Syo/5eyqEzIZ6\nQAnlBS6LCExp0en3JWzH51AZ8KqCn1qZKYgRk+lS8HWLTqImW6lfgO1Eme68HzsrOX4iXNULqXaz\nkU0Ovnnhoce3MiaTkILYHvxGQ2jnjDQKfs1y8KWUb8JOjn2V3/0rAH+1y8e5BsA1u/lbQmLRjWHR\nybTJNob/PHeLjrp9rYYYPne/jcb210AbdpWFgq+fUP0q+HqjcS4e/LELnGaYJltXsx49+KQuRMnB\nN44TrcQrfSHSi7Px4BMy76iFXF+GuWLXE0TyUfBd6ovfHHzdAlGg5+CnTNGxRyT6vMjpV1DwU02z\nVV/6RkOE8+Abzbz6oKs8VnB2CgpFMQz0/BWnGmMySW2I4cEfs/IlTtvyvUIBsMAnJBpmERNCUdMz\nwPNR8F2FtVfl0qHgtzOJSVSf63Ig25B20mraYzKTWXSMAjeYgm/EZC5lUuBrKU8NgXZDVfDDWNX2\naFn7bLIl9cCaouO5wDePR8uqEJRk0JX/+2SBT0gkzGI2RLFZFpOZ0p7gKuBCTXHVmmxzsei4FHyv\nHnx7Dr6WopMoB1+bUyBCKvjG0ntDDFd0en2ZbB8wL0DbrfAe/D2BhmkREgopZXwFv9nQPo8phKC5\njskkZN4ZU/ADqARmTOayqtwmTNFxFVWhcvBdg66SWnTUDHR1yJHHixxnik4GFh1TwVZXGHwOedEU\n/GYDQug+/FSrOFoPwrDJeIdQF3nqhSQtOqQOxJrjYH4el5rpLoZdFzWzwgKfkEiYMVghik3Tf7yS\niYLvbrINn4MfKo5wWtQDuD7kKEKKTgYWHfP90Qpcj/tBx/gMAMgiC19rsm0ItDUPfiAFf4kKPqkX\nLiU7pILfMj6PsS+GQzTYAizwCYlGDA++ep/NhshIwY8bEakV+Injzwq6mrIaXsFvOC066VN0GoZF\nx+fJ22ZTyiELv2sq+KFSdBwWHcZkkjrgtnOGy8Efb7KNu9KrngPUxvhZYYFPSCRiePDHMsAzUfBd\nCqXPwkZVSJuOQVcpLTrqS6A32YaJSNRTdJSYzFQWHUPBb4by4BtNtoCh4Cf6HPSNgkK/8AyTokMF\nn9QNV4EfcpJtq2kU+JGPEfrp0V+FzwKfkEiYSm3oFJ2WoeDnEhGoEqzJNkOLjvpcVYuOz4scVw5+\nDhYdM8JVjYkMNcl2aNFppfeim4qhfuHp8XPQsyv4nS5TdEj+JFPw1YnnkY8Rqi3Jo4DPAp+QWIw3\n2fo/4XYMBX8lkxx8VwHjt8nWoeBnYtHRU3TCWHSqpeikV/AbQmhNwH4V/PHXIIcs/LEeBOUCJ9Sw\nM6bokLrhLPA9779mqtdSoFXVKmghA7ToEFI/zANXCIuOueyYi4LvUqljTHFtJzxwq7hiMkPZMzQF\nP4MUHVMxCzVh2LzIBWBk4ae36DSEYR0L1IOgvu9bLPBJDUhh0TFX1KjgE0KmwizkQjfZthqGBz/p\nJNvwy649I6WkIFQhOS3qc1Xfl1BDjtSUmhwsOrqCjWAefFuztf45yETBD7Rfdl0XkozJJDUgxrnC\nvD/Tgx97tUs9ZrHJlpAaMh6TGcKDr6qXIptJtu4cfH/bpFt0Rt/XlJkMFfzQxS1gWHQ6aQZd6Qp2\nuBQdrQ/F4sFPpWSbKULq+9OX/l4D9yRbFvgkf9Io+I2kSVv6c2OTLSG1w7SpBCnwVQXfSNFJ6cF3\nqjIRYjJTKjMqbotOKAU/Lw9+11hdCJaiY1nFyELBN/ZPIfTGPl/7gfr89y6FsYIREooYgQzm47TG\nYjJp0SGETIFZxGwHbrJtNTJS8F3TCT0etM0mzoJcLDr6oKswGej6+PXR81b3g81UTbZayhEMBd+j\nRUWbZDvIwc/Ag2+7ANUabUMo+EvMwSf1wjXoyudxEjBmhgS62K6Kemz0WeG3Jv8KIcQHpjIRPCaz\nqdsAUimXgLuw9qvgj25rTbYZ5OBLKd3e6EBRobqCPzrUb6Zqsh3LwQ9zkaN+rtpDBT/9ha45BwAI\nc/HpWilKFQ9KyDSkiMlsNYUWxhD7s6I+N58KPgt8QiJhFvRhLDq6PUGpH3A+5aCrCCk6ribblEuv\nBeq5aSdBRVGvvSr4+iTjghwsOtoFWNBJtpYm23a6k3eB+fyBMBef9OCTOhPLg28mjqVU8H0/twJa\ndAiJRAwFX1WDx5psEyr4rgOYz3hAVw5+Dhad8YjIMBnwrhSd5VZjmM6w3e0HO6GU0TcuwELYU8yV\nkqxy8C2D2NTXIIQHf89SHv0nhFQlloLfN44Tbe2zKHXbTGDUY6PwGKPDAp+QSJgn2BCJLj3Nf2w0\n2SZU8F0NUqEiInPLwR/PXA4zfEtXpUbfF0Joam4Km45pHwqh4OtJNaNCOgcPvmbRsSj4vmxKWg5+\noHkLhITCreD7HnRV3vQec7VX/eyzyZaQGjKm4AdQElVFvG022SZU8N0WnUA5+IoKkuqgraL1RjQa\naAUo7IDx6DcV3aYTPyrTVLBDTLLVs63VFYz0nwPbBag+7Mq/gk8PPqkbzkAGzxeoPYudMVXiWt/R\nWDwrLPAJiYR54Iodk7nV7UEGOpBMQj2YqtsUzKKjHNlysOiotVtD6Nvks8m2a6QoqaiJKimSdLQC\nd8yD7+c10BtsR/e/nEEfhtWio+2bYT34TNEhdSCFB781vOAefR5jXhBz0BUhNSdOga8nA7SajeHB\nqy/TLdOrj6sWmqGabFV1eGf5dee2z4FC06CnGzWCWDMAt00JQHqLjpmiEzgisuko8LNQ8Aeb1gqR\ng+/4rNGDT+pAihSd4nyxlMjO6fu5FbDAJyQCUsqxA1cID77WZGsb8pPIf6wW8qF8wS4FXwhhpJXE\nL3TGmmy14tanB1+1ApkK/ig0LUWSToxJtur+1NYsOuk/A7YL0KUAvRjOFB1adEgNiOXBt0UKa1PP\nI35etCZbTrIlpF7YrtBDK/ijiMDRSf58IvXS5Qv2WdyaFhAV1a6RpMA3GizVhBufFzllCv7exMOu\nxnPw9dQKL4/hVPAzyMG3XIBqvRgB+hBW2GRLaoZ6nFgKtNIJ2I8VeqRyvGOkdlFDiw4h9cKmSvhW\n1MyIwMJPuJKBeuks8L022Y5uN4ziNnWSjnnhpXo9Q9mUWkaTbUoPfr8vobZ/mH0IQTz4qoKfQw6+\nZdCVlqbkabu0FB1jkm2qHhxCquLq1/JtYzFTdAAjkCHApHkXmgff4/2ywCckAjEUfFvsF6Ar+KnU\nS92iE8Yuo6UimAq+pgSltegUvRHD7fGZg69eSDRNi46SohPZg28Wt0LESNFxePBTWXQMixJgpuj4\nfw3aSg+O+TNCckQ9PKsX5mE9+DYFP1GTrcf7ZYFPSARsRaVvD74rQUXLwk/QXAmUWXR82lNGt017\nimrRSZEmYlp0QlmGbMkQBSst1aoVucC32Kc0BT9ABry6DyxlMOiqb1XwlQLfm4Kv7wOpfMWE7Ab1\nM6wq6t5z8NXEOYuCH9PKSYsOITUmjoLvsifkoODbG/98qunmpFSV1BYdUy1Si89YKTorihq2FbnA\n19+bna/NAMqy1mSrTfJNn4Ov2bSExaITYJJtyKFqhIRA3X+XA9k5gZ1EtQLbBXeymEw22RJSL2wH\nJ+8FvhGRWZCdgr8UpvGvtMk2sUXH9OC3AlgzgAkKfsJm64kKfsyYzAwm2Q5z8APsB6aCn8p2QMhu\nUPdf9XPrPwdfHz4IJLTocNAVIfXFlhbju8DvWA5YgF7Y5eDBV60ioVJ0TAW/ldqiU6Kqem2yrZiD\nn9SiYylu/Sn46iqWPUUn3aCr0W2rJcDDZ9OM4202zIhYevBJ3rgK/JAe/OJ0qRX4HHRFCKmCTX3w\n3aXf1TLAXUN+0iv4e5bCRJ/1pVvBTzXApEBPt9FjMn2+Bl2HBx3QLTqxB11ZC/wAKTq2ZAzA8OCn\nGnRlU/A1m9Ls22W+zkLoCj6z8Enu6AV+GDFo5/7GE8eWsrDo+IMFPiERsBWVsSw6WSj4anSfms0d\nKgffOLJphVQSBV8vvDVfdLDXICOLjqXBNIQHX/8MuAZdZZCDX3jwNUvA7K+B7QJH8xXTokMyRz2G\nqRenvhrxAXtsr/l4MftV+rToEFJfrDn4kSw6yxmol2rhFS4Hv6TJNnGRoyccNXR7ilcF352Drw08\ni+xDV69hiohI9SLUl79WbzRXVrHUBuMMYjJtuds+Ljxzms5JyG5Qj+OhLDrmiqqwNL3H/Kyo5wDh\n0aPDAp+QCNiK+aAKfkNV8JUm20TFTcdR4Pu0y9gU0oKcLDqNRpgGU5cqVbCSsNk6moKvFdH2FJ1U\nRa7WZGtpNPZxPLAP72GKDqkPmkUnUA6+a6UzlYLPJltCaozVg++50FQPSLo9IX1EYM9h0fHbZDu6\nbdpT0lt0dGW9HSBv2UzQMZUgddBV7P2g1xs/oYbIwdf6UDLLwe9aLGS+41snKfhssiW54/TgB5oX\nop4r1M9KzONEnx58QuqLNQff8wFEPTC6mmxTxWSqEYChLDq2QUIFIQrqaTBPKKo9xZcyVea/B/T0\nouhNtlYFP8AkW+W9dcdkprrIHV9d8D3wTG+yHjxG4n2fkGnQPPjaoCuPCr5FcADSxWRqxz+m6BBS\nL2zqQ8hBV7pFJ4MmW+W5qik6Pl+DsgLXdzPjtPSNbVOHMPl6DUxfqclKLjGZFnuKr5WcrnaRO3qN\nWw0xtCz1+jLJKk7fpuB7Lr5tCn6q6D9CdkM3gkXHda5c1mJr450nVHGKCj4hNSNKk22lBJH0DYZ6\nDn6gJltz0FVii06pgu/pgsOlShVovRhJJ9mG9OCrNrXR/Qshkmfh9ywxrr6brc2BakD6BnNCpiHG\noCt9RXH0GPpnJd4xUs/BZ5MtIbXCNqXStx/WlYOfMh6xwNVk2+tLSE8NRqZKrpLapmAqq7pFx5d6\nbe/BKMh6km0AD765D6TOwlf3z8aw+PY7gE29kCr2saUWm2xJfXDFZHY9nitsK13m48XsV/E9pbeA\nBT4hEbAN8vGtplWKyUyk4JvKqp4eEsCDbir4iVN0ukZxp1t0/Jy4JnrwEyr4tinDoVN02mZMaGIf\nvk3B1wqYCDn4LPBJ7piBBOpxwlucrkMISBUpy0FXhNQY28k7VkzmslbYpc/B38mB969gl+bge25m\nnJa+oRg1FE844OfEZabomOTiwbelu3ibZKs22Tb11yB1Fr66240m2Yb34LcT+YoJ2Q2mUBFCCKgS\nkxlTBNBiMtlkS0i9iJGi0+3Z/ceq5z2dgq9bB0wF2wdlk2xTq5g2ZVXzX3s4cWkqucXHqRX4kRVs\n28VXeAXfKPATZ+Gb04wB/xYd9UK6YVklSPX5J6QqZuJWiJkhLjEkWQ5+jwo+IbXFWuD7zsHXimjF\notNOa00AjIuPsSZTTwp+SYGb2qKjFXfF1MSAQ45azbwUfH0I2c7XICduR6M5oEfuJbHoKA9pU9d9\nWHR6ln1g33Jr+L31bRb4JG9iKPiuSOWlVBYdpugQUl9sRex2r++tach8DLV4VBX8VDn4Znyhb/Ua\nmJCDn9iioxV3zTAJKjaFWMWcZOtz35uEbUlcO3H7arItsSmltujYJtn6Xlmy5eCrBf65892ZH4OQ\nkJhW0xBDCl0e/GQKvpaDzxQdQmqFq4j1m+2bs4Jv5sD7L7hLm2yTp+iU2zM6Hjzokzz4rWZj+P2+\njBuZaIswVfdRH88fcNvUAKPJNkEviu0iR93GUJNs1QJ/7Xxn5scgJCTmhXArwLArlwefTbaEkKlx\nHZh8FpuumMzlVgYxmUoB124K7+o1YG9iHD6m57SSadGL751t8a1g6xdR9kP7nkRRmTbrSDvALIBO\n3/0aLLfSDnyz9SEseVfwxwuXfStKgb9FBZ/kTVniWpB5GRlMsu0FWk1lgU9IBFxLiz5TLbqOmMyV\nxNYEYDz6LESKTt8SQzh6TL/NjNNi6w9Q3yPv/muLgg8Ay+pU44h2rUnP398qjt2mBqRLyCjoW1aY\nNA++h8+BbR/YT4sOqRHqocD04IdW8JN58NUmW6boEFIvXMqDz2JTn2RrV/BTWBMAm0UndIqOu7hL\nPujKomD7tujYPPiAmYUf73Ww9UdoCn6EJtvU8yBsKU+aRcfDxf4kBf8cFXySOWYgQQgF3xScCtSh\ncFTwCSGVcCm0fi06qg3G5cFPo+B3jG1rBbBn9Ety8H2r5dMyMSbTwzZpU0ydBb4alRlTwR/dblo8\n+N6a58qabFNbdCyrGJrn10sO/rj1QGuyZYFPMqdMwQ+RuKYr+KNjREwhqK958NlkS0itcCkPXgt8\nx0ErBwXfVLBDNFiWN9mmtejY7Bn6NF+/GehVFPzNiJGJtkm2IaYZlzbZJm42V1U6ax+Cj1UcSx/G\n/hVadEh9MAMJ2gES11znyraq4Ec8RqjbQ4sOITXDNanTZ4GvqeRqTKZqy8hAwW8ZKTr+mmyrWnTy\nUPDbntMhbDYgkz2JsvAnTlj19DnolCj4qfy1BZMUfB8WHXuKTnv4PTbZktxRD88pPfgxzxN9WnQI\nqS+ug8W2zyZbh//YPGj5OkhOg158mhadAE22pRadxB58S0SiF/XWkoFukmqarS1BJoQHv1fmwc8o\nBz9GVGpzcN9U8EmdMBX8MCk6diEgi5hMKviE1IsoMZkOBVcIkbzB0FRWNeUyiCqj/0wrpBIU+F2L\nRcV3o3GlFJ1EQ8+sFqUgCr49/g5Ib1WzWch8r2LY9oG9S81h0bDZ6SXZ/wmpijnoSlfww3rwlxMl\nbfmch6PCAp+QCLjUuWBNtoaCu9JOW9x0DYtOiOmEao3cMD34Wr5x/BUMWwOs70bjrsUCYqKn6KSx\n6FhTdALYtEoHXSWx6IxuNyw2Jd/7QPE6CyG0Rtt12nRIxowNugqcuKYeJ1KlrbHJlpAa03NZdCIo\n+EDa4qbfl1AFimbDaLL1laJT4sFvJ7bo2BpgfTcaV1HwV1J58G2TbNX3xNskW3v8HaB/BlJ48G1z\nGlqeV5ZsKTqAnoW/RpsOyRjzPKZ+RnzZS9XjjSqGJLPoqE+LFh1C6oU7Rcefmqw1sjbdCn7Mwg7Q\nn3u7KSCE8J4eAtibGNXHLUiTg6/7SgF4bzS2+a9NUk2y7VvsU/p7IiE9NJqZzdwqqW1qNnXdd+Ov\n/hij+2YWPqkLPWMfbgbw4LvEkFQKvnp+8Fjfs8AnJAauIrbjUSVQi0RzimdKBb9rKW5D5NKXpei0\nE6fo9CwWHf/+a3dxW5DKomMrPIXwn5BhNnOrpM7Bt60wtTxHALoKF2bhk7pg9qq0AnjwXRfCZkNv\n39MFxSS0JluP98sCn5AIxGiy7VgK6YLlRIUdYCj4g4Opb2sCoBfRZRad1JNsixNK27NlpFoOfiIF\nX44r+ID/LPxOmU0tdQ7+hD6EkLMQ9q2MojKZpENyxvycNAOIQX3HhbAQQlPxY81M0R6GFh1C6oUz\nJtNrk61qhTEsOgnVSz2+czxBxteya7/MotPyf0ExDXrhtfPVtz1jag9+1Em24/5zwFjF8NKH4F7F\n0F/v+BYdWx+CmaQ0q03JtQ9oHnwq+CRjzAI/dEymaWdc9jxdugq6RYdNtoTUClfOrU+7iGqFKVcv\nIyv4PXVlYVzB95eiU6Lge04rmRZ923a2xbdSZHsME9WqlXqSLeA/SadT1mSbWMG3WXQaRgzgrAWM\nq3BhFj6pC2avSivEvIyyqecJmvFDnZJY4BMSAfXApDY6+p1k6y5uVlpprBnAeJPtztew0WdjB+1G\nfFVGxaas+k51qaLg71lSV3Ii5uBbEmQAw4PuxaJScpGbOgffYVPyeZFji2MFdA/+2vnOTI9BSEjM\ngXDhPfjulb5Yq719hwA4KyzwCYmAWnjsXQpT4PcshXRBWgV/3KKjL7t6mmSrqcT6z1SLToqIRGuC\nimcFv5IHP9GFnktZVpvBfQw8K7vIUS+oYtqTClwWMp8Xn/o+wBQdUj/GB12FCGRwW/lSnCu6TNEh\npL6oBY7qg/Z5ACmNyUyo4OvTRQuLTgAFv6JFJ0mTraX4Tu7BTzzJFvCv4HfK+lBSD3tzqIZtj9F8\nrsJlH3PwSU0oU/D9WXRGt00PfhoFP8z9ssCfA7a7fXz59hM4vbGdelOIA7eC79GDXxaTmVDBtxWe\nYaaYjm6bFp2lxEOObBcfvoeqVMnBTzfJdnRbLW5bRhb+7I/jvsjTnnuKJlvHtulTnT168NUmWyr4\npCaY+3AziAe/pBk/QSCFpuB79Oi0Jv8KyZ3f+eh38F+/chceeWAZn/s/nqN5TUkeuBR8nwqB3mTr\nzgCPruBbVhZCTDE1lR+VpQArBtNgu8jx3mRbKQd/tB9sJp5kC5hpSh4UfOU+xmxq6mcgYoNxgWv/\n9Lm65LqI2LfMmExSD8xmdFWs6nk6X7py8AFgSTlusMmWJOXhtS2876t3AwAePLuFm+5fS7xFxEYv\nQoHfsXjdC1JO8TQ9lYB/5RYoV2+XjOgzH1NTp6Gr9QdYCnzfCr5DBUqWg+9Sr9X9oDv7e+LyoANm\nRMK75dsAACAASURBVGjiHHwtKtRfhGvXYdOiB5/UBfM41gwQqVwWyLCUYCii1mTr8X5Z4NecD339\nHm2nP762lXBriAu18FAtOl5z8EuXHZWDlodCahq0omOYouM/JrNvKaLV/6uPGTtJxzZYxb+CPzkm\nM49JtiFz8N19CKmee4FamzScCr6/HPymy4PPAp9kzFgOvnLc9jHtGrCfkwp8Wyen3R4OuiIAACnl\nUL0vOH6OBX6OqMW3FpPpsdjulsRk6gp2uhz84STbEKqMI4qxwHdT6zR0LVOGlwN68M2TVoHWaBrx\nNXDbU3zn4KsWnRIFv9OLvopTadjXzAq+/SJfz8FnTCbJl/FJtv49+K4VRcBU8BPEZHq8Xxb4NeaG\nu07htofXte89TAU/S7Qc/EAxmXpxk0+TqU299WlLKCiz6ABpBpgU2Io73+/JpOcPGB78RIOutBQd\nLQIv7GvQbjaGRW9fxu/FcG2bz8+CaxVHLfCZokNyxrSZ+WxCdz2GinrBHUsEUcUpn022LPBrjKne\nA1Twc0U9MIUadKXZEwz1MsWyY4FNWW4FmCzbdzRyFqRstO1ZXgPvKTqWXgeTlURZ8K5JtpoH34M6\nV3aRC6RrMgbKpvn62y9d+4Bq0aEHn+SMdhxvCE0E8DXoqrRfy7N1ctrt8QkL/JpybquLj33z/rHv\nHz/HqMwcUT/AaoHv8wDSKSnwUhy0CroW24S6fT6818BkBTvlKoZNWfW9FNyz2IBM1NWjqDn4mn1q\n9P225xz8sgmVgO7D34pd4DumzLY89qO4PgOrS6MCf2O7F6ygIGRWtGnUhgffl0XHZpksUK2TnVgK\nPi06ROXj37wfG4MldvVkQYtOnqhFbLAc/L7bf6wXt+mabG0Z8D4UfCml3sRoOUrqFzmR+xAmWXR8\nFPiOAlLFHHgWy4deJQPex2dBfZ3NzwCQNi7WOcm26W8/cPVhNBqCKj6pBepx3PTg+7ownWTlK0ii\n4LPJlnzh1uPD2y986iXD27To5IkWk6kW+B4Vgm7FmMz4Cr5adI3HZPrIP9fsD8LuY1xK4K0ssJ1Q\nfG9PlRSdRkMkeR1c2+Z7wrCp/ploKxixB75VyMGf9WK3rHBhgU/qgKmu+xYBgPK0rRRNtlTwicZd\nJzeGt3/yCY8Y3n6YBX6WaDGZwXLwx9NqCvQEmdjqtVp0jafoeJlgWjLkqmA5E4tOcXHjPQe/ggcf\n0Kcax7Lp6IXn6Ps+L/T6fTmm/pmkjMqMk4PvvsDRsvDZaEsyRT0MjCv44QddpehX05psPZb4LPBr\nyj2nRgX+D15ycHgwXzvfTZLxTMrRYjKD5eArB60Msn0LbIWn7xx89bhva7Ddecw8Cvxi+3xfcFRJ\n0QHSDLtyTrL12WBqqHK2VRzVohQzRQgw5zSMvq82nM/aaFy2iqMr+IzKJHmiKfhC6IEMASw6ZQp+\nrBXOPi06pGBjuztspm01BC4+uAcX7lsa/pw2nfyIMcm2TMFN2mSrqoq2FB0PB+0qCn6KCYUFtlg2\n317PKjn4gN7kHUsMcOVO+8zBnzYmNPY0W9c+uuSxqa8s/o9RmSR3zF4q06ITYtCVORQxhUWnS4sO\nKbjn1Obw9qMu2INmQ+DIvuXh95ikkx8dbZJty/r9mR+jYpNt7Em2WrrPYLvaDX+2BKB89HhByiZb\nqwffe4pOVQU/flRmzzhpF+gDz2Z7Dcr2/4JcLDqNQBadsn1AG3ZFDz7JEFsvVYhBV70yMUydeJ5C\nwfdI0AJfCPEiIcSfCCE+L4Q4K4SQQoj3TPibZwkhPi6EOCmE2BRCfFMI8TohRLPkb14mhLhOCHFO\nCHFGCHGtEOJn/D+jPLhb8d9fengPAOgFPpN0ssMVk+lzyFMhEApRnu27FVnBty2H+s7B75coMgUp\nJ9lG8eAbS9suklh0HLF0Wg6+zwz4CpN8Uxb4ekymP4tO2SqeatGhgk9yRF+BGo9U9mHnBMpXfJM0\n2WqDrvzdb2gF/40AfhXAUwDcO+mXhRAvAPA5AD8G4EMA/hTAEoA/BPBex9+8FcA1AC4G8C4A7wHw\nQwA+KoT41ZmfQYZoBf6hvQCAi/arCj4L/NxQDxSaB99ToalFZFoSVFIWtx0t2WRw0PY84Ghai07s\nFB1rTKbn96Sygp/Ah+5uMPWXg1/WYFqgFvhbEWMybdaDAp8WnfIUnfbwdt2bbLu9Pt744W/hFe++\nTjsfknrTtxzHgyj4FSfZxjpX6had+jTZvh7A4wAcAPDqsl8UQhzAToHeA/BsKeW/kVL+O+xcHHwJ\nwIuEEC8x/uZZAN4A4DYAT5ZSvl5K+RoATwdwEsBbhRCXe31GGXC3YtG59PBOga8q+MzCz4/QCv4k\n9VJv6EyXAV/YEdSLED9NthUK/IQXObYTl38Fv5oHfzmFRUdtgnbm4M9Y4PfG1T8T1aITc5Kt1kNn\nxLhqCuWMNiVbv0uBmqKzVnOLzvu+djfe8+W78JmbH8Yffep7qTeHeGLSzJQQHvwcJtnW0qIjpfyM\nlPJ7sto0lRcBuAjAe6WUX1Pu4zx2VgKA8YuEVw2+vllKeUr5m2MA/gzAMoBX7HLzs0VVLB59qLDo\nsMk2V6SU2gFlT4BBV5MiElMM7yiwqYotj82VgKHgV/DgR2+ytaxiaHn03j347kO7rmJHarJ1vD8t\njyk6lZpsW2ksOmU9Im2P+2XZPrB/eX5iMt//1buHt7967GTCLSE+sQk1YRR89yRbXQiKc56oq0Vn\nGp47+Pr3lp99DsAGgGcJIZaV75f9zSeM35kbbAq+btFhk21OmI1DywE8fpMaDH2rxdOgWXSKJlvN\nohPHnrKUcBVD2wca49vT6c0+VbZqDv6eJB58V4KMP/VamwNRyYMf73OgXuCYPSI+G87LUnS0HPwa\nx2R+94Gz+MY9Z4b/v/PEBkWtOcGmrAdJ0enlo+D3lf4537Qm/0o0fmDw9RbzB1LKrhDiDgBPAnAF\ngJuEEKsALgFwTkp5v+X+inW7x1V5cCHE9Y4fPb7K38dCSol7bB58WnSyxWwc8j29E5hs0UmrXisW\nneFB23eT7ei2S7xeSrmKIfV9ABgNcSkapLt96SxMKz3GblJ0kgy6siv4s+4HukXJvhOkmmRbquB7\nPB4swiTb9ynqfcE/33UaP/nERybYGuKTSQq+r/Nl33I8LvDZE1OFKv1juyUnBf/g4OsZx8+L71+w\ny9+fC85sdob+yT3t5tCac4RNttlieqNDeMFtjawqKRV82wCutu+IyCktOilTdFwNlrNuk34RUS1F\nJ5YP3VngexxDX2UFQ109i2rRKTmJh7LolCn4dU3R2er28KGvj+d1fP3uU5bfJnXDFkagns9iePBj\n21mrCjO7IScFPylSyqfbvj9Q9p8WeXOc3H1yZM959KE9w2YtrcmWBX5W9IzlwHbLX1FTMKnBMmWD\nqeo/L5prfWZ/A6YFJr8C33VCWWo1hkX2dreP1eWxP535MUxynWQbssG0IFVMpnkMUPHZaFyagz8H\nCv5/v/FBnN4YtxfdcOfpBFtDfDOxXyvyJNsYMZlVZrjslpwU/EJxP+j4efH94pM87e/PBXefUjPw\n9w5vX7CnPfxArJ3vRs94Jm66hj8+jEWnPCLQtKfM6veeBtvFx3JTafT0UGy7mjhV2p6bWqtieizV\nt8en37PXL1/FKVhJoGK7Uo685uBXaDJO5cGPlbttyxEv2L8yismsq4Kv2nNectWlw9vfuOe0N3WX\npMNa4Ef24GvniQhCUFnfzKzkVODfPPg65pkXQrQAfB+ALoDbAUBKuY6dbP19QoiLLfd35eDrmKe/\nzugZ+HuGtxsNwSSdTDGVVT0WT3qJyNKjKMc/1g3jcWN60G355N4jIksO2AXLiab5mtYZNSLR58pK\nldcAAFYS+ND1Anf0fTUu1WdMZtvx/FNNstUGsQlTwffXh9Cz2OEKtCbbGhb4J85t4Qu3HgewkzTy\nq8/9fhw9sAIA2Nju4eYH1lJuHvGArdgNnYNf2mQbocDXwgFafkvynAr8Tw++Xm352Y8B2Avgi1JK\ntXIt+5vnG78zF7gUfMCYZssknWwwD1pC6D58HykyVaZ4pmq01betMbYtVVYUvnbsJF7zX2/Ax79l\n66c3UkqqePB7aRosTfuQXwW/Wg6+GhUZa9hT36Gu63Gpsxb4ky06e1JZdBwXOIBuV5t1HyibZlz3\nJtv7z5wfroRd+Yh9ePShvXjaY0YtdvTh1x/bvJCW55kpQPlQvBApd6XbUjH9bDfkVOB/AMBxAC8R\nQvxI8U0hxAqA/zz47zuMv3nn4OtvCSEOKX9zOYDXANgC8O5A25sE3YNfUuAzSScbbIVH26M1AdAv\nElz2jFQe9I7lANZsiKFVpUiQKeM3P/Qt/N237se/+9tvWKevVorJTNSHULYE67XJdhce/FiTbG3N\nc4CRIDOjOldmTylIZtGpmKLjVcGfkKITarhOKNSG8OK5PPXS4WmfPvw5wLYK2Qxg0XFNlQbiT7LV\n4339luRBm2yFEC8E8MLBf48Ovj5TCHHN4PZxKeVvAICU8qwQ4pXYKfSvFUK8FzvTaH8WOxGaHwDw\nPvX+pZRfFEL8AYBfB/BNIcQHACwBeDGAwwBeOxh6NTfoCv4e7WcXMUknS2yFR7vVAAbFVafb3xnJ\nNstjqPYEl4KfqMDtOZofl1qNYZHV6fWdBzcpJW57eB0AsL7dw4n1LTx6Sb+47ZXkjKuPVxD3+cdZ\nDnYV0SYrSSbZjs8BAPR9dWYFv1KTbaJJto5JvoDfmMyyi8lmQ2DvUhMbg+PO+nZX8+XnzoZyMbp3\naad0oYI/X9gU/HaAJttuiSAWu8nWnN9x3uN9h07ReQqAlxnfu2LwDwDuBPAbxQ+klB8WQvw4gN8C\n8HMAVgDcip0C/o9tE3GllG8QQnwLO4r9LwPoA7gBwFuklB/z+3TS0u9L3GMZclVwhFn4WWLr2Pfd\naGublGqSTMF3KKtLzVGBv93tY+/S2J8CAM5udrXXcMOiOmsWEEdtm2IEOVA9scGnRad6ik6CSbZa\nTKbHHPxKMZk5WHTMAt/fap6W1mP5IOxbbg0/P+e26lXgb26PbEXFPIMnPeog2k2BTk/i9ofXcXpj\nGxe4DiQke2xJYCEUfPVzYp4uNctcFAW/vH9uFoJadKSUb5JSipJ/l1v+5p+klP9CSnlISrlHSvlD\nUso/lFI6j8ZSymuklFdJKVellPullD8+b8U9sBN/WexwB/e0ccA4OLPJNk/UAr44WPkeutSp4L/W\nHzNecdM1FIrh9ijFVtmB9MS6vi+vW/zDVYpbfek1ZopQ1bHo4RVsIINJtsrqgpaiE9miEyMho6BX\nsg/EUvCBejfa6gr+zvu40m7iiRcfGH7/63fTplNn7Ck6/qJ0R/dTMugqshBkm/Tui5w8+GQCWoKO\nYc8BTIsOm2xzwdb86NuDPykmE9APXHGLG3uD5XLF7Tm5ru/LVg/+1E22MWMyR7fLhhzF8uAvZzTJ\nVvefz/b8O1WabJcSKfjqPpBoki2g+/DXatZoayvwAeDJjx7ZdL73IJN06oxNCGhqNr7wKTqxe5TU\nz/zSDJPMbbDArxGa/95osAWAi2jRyRK9ybQY9OTXotOxJNWYpErR6TgSfqqmh5wwCvx1q0VndNtV\n3C1rannEFYwyv6fHlZwqCjaQxqKjXryo+6EWGRtwimtBihkAQHlB0fJYwJTta0C6QV8+UIutPe3R\nhcojFGHrlGUIFqkPttVOM1baB2VTv/cttYYBEOvbveA2HX1ODBX8heXY8VGBf9nh8QL/yJw22Z49\n38HffOUufPveM5N/OUMmefC95MBrw7TyarLVtk314FdUr08ZBf7GtsWiM62Cn2gFw6y5ln0q+FVz\n8FvxLTrbyrap+2HL5wVOhYtcTZ2L2WRbsn+2Pb0G/b7U0kFsu4BmUYqYIuQDl4J/werImnp6gyvX\ndcYm1Pic9FxQ1qvSaAitj+P0Zth9ymyy9QkL/Bpx+/H14e0rLlod+7nWZDtHBf7v/d1N+M0PfQs/\n/+dfGiv26oDNG9323Knfq6DepipwXept1bQCU8Gf2GRbJUUnWZNtid9z1gK/RJVSSZGio66YqM9Z\nT8gI6z8HxmMyY010LlPwfb0GZQPVClKtYPhgozPeZAsAh/aOetFOrVPBrzPq/l9cCKvnSl8WnUmJ\nYxco+9SZwKtCtW2yJX657aFzw9uPvWjf2M8v2NNGsa+une96GwqRmuvv3Ik/29ju4ab7zybemunp\nWiw6S75z8CsMumonarJ1FvgVVxRMD/7EJtuMFfzSJluPFp0yBT+FD119bnqB7zFFp0KTcbMhtII6\nVi9KWYyrZtebofm7Sg+GdoET0abmg02Hgn9IUVtPUcG38uDZ83jT//cd/Lfr7kq9KaX0LSKF70AK\nYPJnRd+nwhb4eghFjXLwiT/6fYk7NAV/vMBvNAT2LbWGzVPntrpzERl2enP0ATPV3DpgO5iEjMls\nZ6bgq0WUerCuuj1TN9lWSNGJOsm3bMhRy18kWxUPOpDGouNSqfRm81mbbKv3IHR6O8fIrU5fK3pD\nURbjqg/72v1rUG0FQ1Xw6yUAOS06itp6mh58K2/9h5vxt9ffAwB42mWH8ANH9yfeIju2QVemnVVK\naV2dmupxSibZAjtiaUHoi8ZOhYCM3UIFvybcf/b80DN6aG8bh1fthfsBZcdcq1kMmoszaoFfQ+uR\nzWPny3c7fIwqMZmaRSVegaurt/aYzK2Zm2wrKPjJBn2VKfij12CWAldKubtJthEU/F5/tG1C6Ccx\nPQJv1ibbaifKFCp2WQO0r4ucKj0Yc9NkuzTSJqngT+aGu0ZDwG5/+FzJb6bFNi+j2RDa/jzrcUJK\nWTrJFoDuwQ9e4CviR4sWnYVE/VDa1PuC/UrO8dnz9Vczznf0Lva5VPB9NNlWWOZbTlTg6jFgTeV2\nNfV62iZbV3FTNZbTN7aY1AJf0aXmPlamcJmNvf0ZT5iT2DZWcIQjB39Wi06VJCnAmGYbIQYP0C9A\nxwfr+LHo6BalyU3G9VPwR5/7vW27Ref0ZidaX0Vd6PT6uPPEKKDjXMbxqC6b4ZLHFe8qx0qtryO4\nB19dfaeCv5Do/vvxBtsCtcCfBwVfVe+Beub722KwVCXbTw7+lE2mNWqyNS06tiZbPammyvOPGZPp\nTlDx9Z5U9d8DO69PzJkIZoGvEioDvrJFKdJ+UHYB2vLVZFvFg1/nJluHRWel3Rjuz9vdftR0pDpw\n98kN7fhg62HKBdfMFJ/TZSc12ALAodV4q0JdNtmS2x4e+e9tDbYF6ujxeSzw62jRsfn9vOfgV2gw\nTFXgztpka06ytSn42tKuo7bLI0VH37hlT9tUpclYZU9Eq4arwRbwG4GnDXsriZtLoWL3yi7yPK2s\nVfHgL9e5ybajWnRGz0MIEVVxrRtq7QDkreD3HL0qPo/dVS6EY6bobPcmr7ztFhb4NeH247uw6GzW\n/0BnNk3V0aIzsXHIS5OtogI4Ggx9P2ZVqij4rsJmY7s7VoTZFHz1+efWZFs5RSdwcaei2VRSFvge\nU3Q6FV+DmBc3BTZvcYH2Gsxgl5o2RafeOfh6Pojmw6/hOSIktxme+3Nb+V7YdZ0Kvr9jd5Vj5QV7\nYir4nGS78Nz2kKrguy06BzQFv/4F/nwo+OPFd8gUnWoKfsQC3zHIo4oqc8JiydqwnKB0Bd/+/Hey\nwXduq42foamagT7Le6L+bZVGrZjNlq4LPMDw1s6Yg9+zWOFsLGtJMpEsOspTG0tS8tRkO32KTr6F\nng1XTCbAJJ0yzKbanC06fcc+rNk5Zzx3aYEMjnNlXA8+J9kuNOe2unjg7HkAOzv9pZYptgXz7sG3\nFXy507UcULQcfC+TbCf7+PQ84TjFbb8vtQOYug1Vpvna1JN1W5OtWkA5ihshRJIkHdv49QI1SWiW\n4k69mFePAS5UH3poD36npAHcb5PtLlJ0ohX4JTn4DV2d3G2TaDUPfn1TdFRr3h6jwGeSjhvTopNz\ngd91fE58rj5XUvBjpuj03cfHWWGBXwPuUD6gj7lwb+lOoHnwM/4gV8Us8Ne2urU7MfUsXfK+mxyr\nHLRSKPjqwctMUKmk4FuW23ebgz/2mLEK/JImKl/bo17M71ueXODHVLHLmmzVfbXb331xC0zRZJvY\ng29uW8NTDGCVadb1TtEpU/DjFWR1w1Twc/bg2wZdAdXEoKqU9cMUHFqNqOB31fMDLToLR1X/PTB/\nHnyzwAfGU1Vyx+Yr9H2i7VRo1DHjEWNQZs+oEtt50rJiY1Pwq+TgA0ZUZqRpvq5BX4BxoTeDMqWe\ntHNT8LdK9gEhxFiRv1sqx2QmSJKZdAHqw6aj/p3rIlez6NSoybbXl9p+pO6/gG6poEVnxMn17bEC\n1Xb8zAVXGlzVxLVKj1FhXsYh44IxZPRqlwr+YqNHZJYX+PM26OqMRY2pm01Hj8ncOaD4Hrajq8ST\nm0y3IxW3pf7rChcctos5mwe/6pCnFBYd9bUu86DPpuCrFp12yW/ukIuCD/iz6XQrWnRUe0e0JtsJ\nF6CmTWc3qNaUg3vsF3l1HXSlJei0m2MXMBcwRceKbajVuYzrAlczumZpndHK16vgwV9pN4cXw52e\ntAY7+GK7YvrXbmCBXwNuOz6y6FxR0mALzN+gK5uCf3y9Xo22tsJj2bOKWObzLkhhT3E12FbdHptF\nZ6PTG1NUylJKVNqt2QupaVGf27K5iuHpPTmrnLT3V7HoJPLgmxc4gF7czuKvtc2bsKFfXMe36Nj2\nz7YHhfLBs6Pj4iMPrFh/R1XwYw57m5WyBluAFh0XZoIOkLdFxzXPI5RFx2VlA+Il6XQdPWo+YIFf\nA6ZS8Oe8yRaouYLfsCj4Xiw6k2MylzxGjVWlTMGv0jhli7wzl+uL7xVkp+BXfQ1m2B5Vlatk0Uml\n4FsKfF3BD9+HoFp0Yk2y1Sw6FgVfsynt8rP54CCIAXAX+Mutenrw1ffJbLAF2GTr4najwRYA1jOO\nyXQdx0MNxCs7V8RKZqoaDrAbWOBnTr8vccfxahGZgDnoaj4V/LpFZdri+/Q8ah8WnTxjMjX1dhcN\npq65B2ZhNqmAmuYxfVPmQfflLV3TCvwKFp1WvDz0slUcYHIO/MNrW5U8sGrP0QGHRQVIM+xJs+hY\nzro+CpgqBb7v404sNjqj/dum4HPQlR2bgp9zio5rYJ/PQVdVJtkC8S4aNXGuQsTxNLDAz5x7T28O\nC4QLV5e0pUgbukUn3w9yVawFfs2abDuWxiHNouOhyOhUsCf4Tu6pgl7c6ifmpQoK/kmHHctsFJtU\nQI22IUEfQolFxdeJS72Y3zelgr8VuMjVLTrjxdlSSXH7zs/ehqve/En83Du+qL3HNlRL4sE97ouc\nPQmGPfUmWMh8XOjpBf6y9XfqmoO/oSn44/s3LTp2bAr+ue1u0KbRWXBZdHyuvFZV8GMl6WgxwiWW\nod3AAj9z7ju9Obx92YXu/PuCeR90BQDHa6fgjy/B+bbo9DSLTgUFP9IkW59NturB2Gx6KhskpD1m\nM8FFjvL+LpurGL4sOlOm6MS0apSlCAHlTbbv/+rdAIAb7jqNmx9cK30c9VhxoGQVI0WjqSsdpEBd\nmt+tfa6aBz9+/4EPNA9+u1zBPz0H6XFV2Or2cO3NDzkn9253+7jz5Mbw/8XqmZT2aeA54Bp05WsY\nHGAPvbAR66JRXX1vt2jRWSjUE/cFJapUwd6l5vAEcr7T9zIlNRVSyrnw4Nvi+3wraVr0V5VBV5Gs\nCVqDqbFdVRpM1dWaiw+OipaxAn8XOfjR+hCUz+CyUZyEyMGvYtGJqeBP9OBrMZn6a6AWaw+tlV/Y\nqxadg3vLCvz4Krb6GpgRj4B/i87RKgV+TRV8m0VHXbE5s9mJNqU6Jf/2v/0zXv7ur+Ilf/Fla+/K\nXSfXh6/DJRfs0V6jXG06rkFXPmMyK3vw98Ty4Fdr+t0NLPAzR2scq3DiFkJoDWZ1brTd7PSsRdiJ\nmqXo2Ibc+D7R6hcR+Xjwt0vUiUkrCp1ef7j/NgTwqIN7hj/bME5Qm4plZ8Wi8A0fM3WTbclFziwr\nCqo9ZdoUndAKvt6HYcuAt190SSm1ov14SYHf6fWxPigChQD2WWwcBer+sRmpyFUvoqxJQjMqlP2+\n1C6ALtrvsOgY6V25WjVMyqbYAjuiRrFyJeV8zICZxD/dehwAcPODa7jV4rVXJ9hecdEqVpXjQq5J\nOpUGXXmMySxrao3nwWcO/sKiTqOtMqES0Jfo62zTUdV71XVRNwXfmoPvucDShmXkmqJjHLwmJcio\nS8+H9i5p3nJTwVf3iQtX3X0qSS5yKqbozKJMTT3oKtMUHfU12Oz0tM9OmTVPFTIOrLRLV3H05x6/\nF2V5ooI//WfzxPr2sHC5YG/beZHbajaGRU1fxjsOzMqkmExgsZJ0pJTYUD6337737NjvqA22j71o\nH1aVi95ck3RcVjafMZnqubIskCFFig4n2S4Y08bf7fzeaMc8u5nnlXoV1AJfVW9PnAs7Wc43thx8\n3xMlc1XwyzLQy5orAd2ec3h1SVPuzCbb48rvHtlnVy/Nbchu2FfEFB0tTSXwvjDZomMvbs3Vx7IC\nv2qCDpDGpqIV+O3yi5zdRIVWsecU+B6yFwPdomN/fxcpSWer29eU6O/cd2bsd9QG28detKoJhLkq\n+D3HPJdQFp0yD36sC0ZVxKCCv2Cc25pu6R0ws/Dre6BTr5ovPrgyVG62e31tZSN3dM+fLSbTg4Jf\nIUs3RZNtaYrOhAuOk0aBv6oU+OMK/qj4u3BfiYKfwKKj2TN2ERVahXOala+KRSeigj9hCbrtKG5N\nm8XDJRYdVQwoS9AB0jSaqpGU5rAzoNpMiDLUAv8REwv8+iXpaJNsHQr+IiXpmMe/71RQ8NXjQq4e\nfFfalCYGzazgj5+PbcRK0VGP+5xku2BMe+IGDAW/xh5886StFm51sul0tCv0QUym9ybbCjGZqS6P\newAAIABJREFUmfnPJ8V2mgq+qtyVWnSqKvgJ+hBM9dbXe6JeyFez6ERU8CdNsnXk4JuTuI+XfObV\n3y1L0AF0e1ysLPhJFh11P9jNoKsH1IhMh//e9vixYkJnRfXg21J0ACNJZ84V/A1jBfPG+89qCTRS\nSk3Bv+KifbXw4McYdOVK6jGJlqKjbA8n2S4Yu/Hgz4uCP1bgr45OXHUadmVbdgzaZFtBwY8VEakX\nd/p2LU9YUTg1VuArCr7yuZBSao3XlT34WfQhqAkycizrvd+XE/ePXl8OG0yB8gbTgqgK/sSYTPvJ\n27QXlll0plPwlUm2SQr86n0IVVEjMo8enD8Ff2PCJFtAL8jm3YNvChzntrpaJOaJ9e3hZ2J1qYlH\nHljGvuWm9vs54mqAVQMatnwOusoiRWdyAt5uYYGfObvz4M/HsCvdV9vGEUXBL1PzcqNricHSmmw9\nFNvdCp34kzzvIZilyVZV8C9cXdIUKLWgPXu+O7zA2bfcyi5Fp2ySrRDCOfBrq9vDz/zJF/DDv/OP\n+Ptv3++8/3OGCFDWYFoQ06YyyYOvzm1QPyvjCn6ZB19vsi0jjQdfsehYPPizWnQemsqiEy9ByReb\nFTz4sZoic8BmsVF9+KZ6L4QwmmzzrAu0mEzhsuj4S9Epm5miCgVnz4eLXtUm2dKis1joJ+/JzXPA\nTjFcMC8K/gV7DQW/RlGZXYsq0W4KFHVNry+jNA5l12Q70YM/eo9NBV+Nxazqv6/ymCGYVOC6eiOu\nu+Mkbrz/LLa6ffzF52533v+09hxALzJD21T0mMwJDabKapcpTpxY33Y2oJ6Zosl2T4ICd3uCRaft\n0aIz/022TNHZtAyqUpN0dP/9KgDoAkmmBX7fcR7z2WSrKfglBXWr2Ri6IaS0D930AWMyF5i1XXnw\n5yMHX1Vh6uzBtx1QhBBelcROlZjM3CbZTlhRUJtsDxkFvqrgm0p/Gbpanoc9w3XRoZ5Qbrp/zakg\nre1ilW/Z8wpSGZNjMu0pOmaTrZTASUfhpqr9UzXZZmLRmTUHX59iW+7Bn1+LzgIp+JYCX1fwRwX+\nFRftA6AfG87lGpPpUPB9xmTaJsu7OLQa/qKxyur7bmGBnznm8nsV1CbbeVHwdwr8enrwtQ+wUnzv\nZqm815f4u2/ej09/90HjMSYr+OrBrNeXUaY96had2VJ01KV5VcHSFfzy4ia1gj+pwVL93Q3lJLzZ\n6eGO4+PDbIDdHSNWIir4E5tsG/bi1rToAMDxNftJ9oxh5yvDHC5m9j2EQG1mtXvwlYucXWyPatF5\n5CQFP+KQM19sdpQmWyr4Y022AHDjfWeH8dG3aRGZOwV+LRR8OW5nBfw22ap/XubBB+I02lbpn9st\nLPAzRx/gslgKvlngax789focwF1NPeZUySp86Ov34jV/cwP+t2u+hk/dNCryq+TgCyGiF7hbVS06\nloO2qsId2ruE1WVVwR/t12o/xpEpLDrRhn1NKHBd74mZ9f+d+8aj8ADTolPNxqclqQRX8MtznlsO\ne4pthofLh392iibbRkNEbzjXPfjlF3nTxgBudXvDVayGKJ8DAaRZwZiV6S069RW2qmA22QI7K5mF\nVUtX8MctOrk22eqDrkbf97n6PJWCr85WWA9v0bGdH2aBBX7mqDn4u4vJrO+BbjcpOmvnO/gv/3gz\n/vLzt2czDKtnickEzKjCaifar95xcnj7A9ffM7zd1Q5a7o/18ozNfNOiFqxmA5E5WddUUlUV7oK9\nbexpKzGZW6qCr1p0yosbtcCMliQ0IUXGdfIyT+Lfvnd8mA2wO4tOVgq+ak/pT1DwHZ97TcGvMugr\nYooQYDRa2y5yGvY+hCqo8wEu2r88UZX0HdEbA3XFTj0OqKgWnTMb2/j2vWfw2x/+Nr58+4ng2xcb\nlwL/nXvPYqvbw12DRB0hgO87slPg1yFFx+VHX5rRwqZSNUUHMJJ0InjwfSv41c4GJAmdXn+4hNoQ\nenNYGQfmRME3VbnV5dEH0+bB7/UlXv2eG/CFW48DAB5z4Sp+6omPDL+hE1AtOuoBZXkXFp2HlQLn\ns7c8jPOdHhpCaCfAsmEZS60GMLiLGAr+don3WAiBdlMMlfTtXh8rjdFrYir4qmVnQ1my1yIya9hk\n6/KXmidxt4K/mwI/pgdfUa+tg65cCv74CdU17EptyJ1k0QF2fNzF38RoNJ00ybY9w8qS7r8vt+cA\naQZ9zUolBV/xSz98bgsv/vMvYX27h49+8z58+f/6idJ0rbpha7IFgG/fdwaXXbgXRQ376EN7hs+7\nDik66nlQfb/8evCnKPAjWHTUY16bCv7isG54a0VJpJOK7sHP84NcBfWK+eBeo8nWYtH5o0/eMizu\nAeCGu06F3cCK6Ck6qgd/eiXtobWR13Zju4cv3XYCn/7ug8MC4uKDK6UTj2M32k5afnQ12m5u94bP\naanZwN6lpj7oyqXgT7AnTMreD4E2yXaKFJ2xaZWKx1ZFL/CrWnTS5ODbTmDqZ0LdB2zHrmoWnekG\nfcXwoU81yXbKAubBKfz3QJpBX7NSpcBfXWoOFdBObzQb4vRGx7n6VVfUJtsiJQfYOUZo9pwj+4a3\n62DRUS+21c+JT2ulLbbaRYy+jm1Hj54PWOBnzG5O3MB8DLqSUo5ZdA4bHzZVGf/0dx/EH3/6Vu0+\n7jqxgRxwNcDuptntobN6gfOPNz6I93317uH/X/T0R5deCMZWsKeKiFR+17TnCCGMFB3Vgz96TY5M\nk6ITKSKwbBUDMGxTJQr+mc0O7jm1Ofb3mge/cpNtPA++ekK22VPMYV8FdouOI0VniiZbwPzsRVbw\nbTGZM1h09AK//AIXqGeKjhqL60rREUJoiqtKLmKPL9Qm2x/9vguHt7982wl8/a7Tw/8XDbaAvrpn\n9vfkwlYVBX/WSbayuoJ/aFXx4Afq6+g6LLw+YIGfMbtJxwAMD76lUa0OrG/3hktpK+0GlltNtJoN\nHB4UcFKOVPzj57bw+vd9Y+w+jp1YH/teCrqOpp5pT7S9vhxTMD/x7fvx2VseHv7/Xz390tL78LnU\nWQXNf920NBc61Gu1wC9UFHWJecMRk3k4R4vOLptsbY10NpuOdpyoaNFpNfzOYShjckymOujKPckW\nsCv4phhQyYMfeZptSIuOmoH/yP1TWnRqkKIjpcRGR1Xw3fu42hSpoha984B6bHjKpQdxxcBnv7bV\nxf/9T3cMf3aFou7rKTp5XtipCr76GfV53rLNpXER2qLTN9LsJl1wTAsL/IzZTQY+sPPBKK4Et3v9\n2qg0Kq7R84/YP1KoCj/uJ298cPj7aorKnSc2smi01YdQOWIyK6jJJ9e3YSbond7oDL/3rMdeiMsu\n3Ft6H7EnuZY12QLu4lb13xfNc6pyt7HdG763WkzmhCbb2Ck6/b6cqGBXTdEB9Kzrgt2s9JlzGEKq\n+NuOxrkCdZl8u2SSLWD34G92esOT9nKrUclrvRw5SWbLYT0oaDmiQk0eWjs/tiqrruo98mCVAr9e\nCv5Wt4/iML7UapQWQUeV5/+sx46U7RvuOpXFucAXqoK/utzCv/3JK4f/V483qoKvioTnMrXu6nGy\no8+ozynsakrVNE22IQZdqaECS81GZRt2VVjgZ4yWoDOFgi+EqL0P/8yGvcC/SCnwCz/6fadHtoWX\nXHXZ8LU6t9W1evVj48q5nVZJU/33Nl58Vbl6D8T34G/tssHUtOgUf19cJPT6Etu9Prq9/nDpVAi3\ngjfp8UKhr2DYD+BLjuXnDYvKZlPw1YJvmuNErLjESRaltkXBP9/pWd8fm4I/TQZ+gRpYsBVYxe5N\neZHnKmA+e8vDeMbvfQrP+L1Pace8qT34NZtkW8V/X/DqH38srjiyin/5w4/CX73sKqwOfv/Bs1u4\n/0z58bNOqAr86lIL//LJj8IPPHL/2O+p/vxl5eJou9ePtoI5DXqcrMuDP9t2nzMujsrQopkDrHpU\nibeeBRb4GbObdAzb79fRh396Uynw9oxUea3AHyhXDxoK1mMUFfvODGw6riW4aZW0hxT10iyWD6y0\n8NNPOjrxPmJbVKZpst1yKPhqo5PZaHvSsPK0LMWT9nhq/nmMmNAKGcdtx8nLpuDbmgV3MysDiNdo\nO+k10HLwB58VVb1Xn9PJ9e2xAW2qlWdSBn5BTBXbvMCxXeSpqxhdx8rSh79+L/pyx774SWUGxgPT\nevBrNuhKVav3Tlidedb3H8Gnf+PZ+JN//VTsWWrihy+9YPizefLhbxqTfRsNgdf/1JXa7+xfbmnn\nSyHE8IIHyDNJ57xDwdcnPc+2EqM+70l1lRrJ6koumoWQU2wBFvhZc26KHdGk7sOuXE1zj1A8pkXB\n+6CibB89sILLLxypFseOp2+01Tz4yoFqecpGP9We8BOPf4RWpLzwqZdUsyZEVvAnNpg6itvTmoI/\nKvBXjUZbPQO/3H8PxLcoTZpgam7T1gQP/kNrW2MrObttxo9m0ZmQAW+bUqk+pwv3LQ9XZvpSn3AM\nuC8GyoipYk/6DAB6AeP6XN5/ZqTaqxfAqkXnaAUFv245+GYxOw1Pu+zQ8PY8+fDVi/+iN+mnn3QU\nT3rUgeH3r3jEvrGLSfX4kGOSzlYED75qT1ot6ecA9BUjNZrZF7p9kQr+QqHuiNMsvQPA/uV6D7ua\nxoP/wBldwcpNwXfHZE5XYKkF/mWH9w4V+2ZD4F//6GWVtiXrJlvNoqMq+KP3Xz3Bb273jIjMyQW+\ndoETwZ5QRcF3evCVE/CjFG+xadPZTZMtEE/B14bXTLToDBT8Tb1oV6ezmj58l52vDFXF/v/Ze+8w\nSa7y7Ps+nSbnsDObc9CutNKu4ionkgELLAHG2CTbJJtoMI6Y9wO/BhuZYBuwsfmwAZOTAWMJySgL\npN1V2l1pg7RpNk6enu7pWO8fPVX9nJrq7grnVFf1nN916dKE3pnungrPuc/93E86K/c8qDXFFrDX\nG0ItJvr1cS5XMP7+sQiz9frD1mTLW3Sc3QcvWdmYCj73nszbSBhj+OCLNxlfv3x1z4J/V2kaeBCg\nVjbGeDFApDDl5HrZmpB7neAy8CUo+GrQVYDhU3TsK3MA0NkSbgW/YoHfudCDT60rS8wKfgCiMguV\nYjKdWnTIVvxARxPefv06bFzSgW3LurBluLPKvyxT1ybbWI0m2xopOoApCSJbMA25qm1P8LvJtlaC\nDGBedFgr+Jet6cUPnzgFANh/aho3bho0vsfFZDop8AOi4HP2lPndrmnTrkRrIoZD50r53mYfPqfg\nB9CiU2uKLWC26Cz8W2iaZlng0+tkd2vCVpOemwna9STlQcG/hCj4+pRXq5jSsMHZlsh7csOmQXzx\nt3fi6OgsXn/FQtGHT9IJVl1gbkSnx7LVLp9bOGdEDeGUF5TEv1/cFFsJCr4q8AOM2xQdwDzsqnEU\n/IF2WuBnkMkXjC37CAP624On4OcqxmQ6s+jQhcxgZzN62hJ4143rHT0Xkc1KdqhV3FVacFil6ACm\nLdNsnstFr5WBD9R5DkCF4s5qkaNpGqewXbqqxyjwnzszY3xd0zTXvTrNPij4xaJWM+c5ZuGv5S16\nMURJATyazOBz9xzC3hOT+OCLN1W8VlSjOeGfRadS4yCF9xgvPC4nUjnuWNJfc6XzpBp+/N1Fks5Z\nF7N26G1LYHVfK46OpZAtFLHv1DRn2wkrtAHfvKtRrReLOgGCJvxVmmILmPqUvFp0SIFfq8mW6/nK\nlZLbRCbd5JSCv3hxstI0E3YP/iS37V5+LYPEY3p+JsP5Twc6mhCNMKzuD5iCX6nJlrvROrPoDNhQ\nq60I2qArOyk6dAS9ucmWi8i08Z6IHJhiBzsKvtUiZy7HRwNesLTLeMzBs+UCP5MvGgV0IhpxpE42\n+ZAkYydFyKq45X31ce7v/rVfHsfuYxPG4y9d1cs91g5+NppWahyk8Arlwp0l6r8HyrYkrlfF9u5F\nmC06ztX3HSt7jPvA3uOToS/wzYt/J+8J9ZwHLQt/rsq0Zzs9KnZJOhBOoxGGRCyC7HxUayZftNXr\nZhfOvih4ii2gPPiBhou/86DgT0vIb5UN760uF26DHbyCbxURN9jRZGzBT6VzUgZ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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ\nmZmViAt8MzMzM7MScYFvZmZmZlYiTRf4kvaSdKakOyT9QtIGSeskLZJ0hqTCMSRNlnS3pJfyNo9L\nmiNpVEHfCZIulXRrHmOrpJB0cD9xfVDSlZJ+IOmF3H9Vk6/1nZK+ImmZpI2S1kj6F0mH1+l/mqS5\nkh6U9Oscw01NxjBB0o2SnpP0hqSVkq6WNK6g7xhJF0iaJ2mJpE05hjObiaEZzpeuzpf9JV0r6eH8\nHryRt3tQ0mxJY5qJpcH4nS/dmy89ecx6jwXNxNJg/M6X7s2X+dvJl5B0XzPxNBC/86VL8yX3303S\nFZKW5phflnSPpOObiWPEiIimHsDngQCeA/4JuBK4EXglt99GvqFW1TanAJuB14DvAH8DLM39by0Y\nY2ZetxVYAbycnx/cT1xX5z6bgCX536uaeJ07AYvyfh4B/hq4GXgTeB04umCbyrivAj/P/76piRgO\nAlbn/XwfuApYmJ8vBfaq6b9HXhfAC8Az+d9nNvtzd76UMl+mAuuAHwHXAV8Drq/Km4XAaOeL8yX3\n78nrlgC9BY/ThjJXnC9dny8z6+RJb34fA7jY+eJ8yf3HAU/k9f+b35MbgLW57YyhzJXh+GjFB2Q6\ncDKwQ037PmwrDD5Z1b47sAZ4A5hU1f4OYHHu/4c1+5oAHAfsnp/3DeAD8n7gA8CO+XmzH5AvVT7A\n1a81f9gjJ2LtezANOAQQqXhq9gNyT97HeTXtf5/br6tp3xE4Cdg3P++l8wW+86W782WHgv2MAe7P\n23zK+eJ8ye09uX3+UOaE82V45ks/+9kDWJ9/BuOdL86X3H5Nbr+dqgNLwN75Z7MemDCU+TLcHu3d\nOVySf0Bzq9o+m9v+saD/9LzuJ9vZ73Y/IAXbNPwByQn+dN7HAQXrH8jrpvWzj6Y+IKRvvwE8VfBB\n3I10NOF1YJd+9tFLhwt858vwyZeabS7I+7u003nifOmOfKELC3znS/fmSz/7Oi/v65ZO54jzpXvy\nhW1fsH6zYH9z8rrLOp0n3fxo90m2b+bl5qq26Xn5w4L+D5C+lU2WtFM7Axukg4CJwJMR8VTB+h/k\n5fSCda0yLS9/FBFbq1dExKvAQ8DOwDFtjKHdnC+t07J8yfNKP5qfPt7KIJvkfGmdZvLl3ZLOlnRJ\nXh7Zxjib4XxpnVb+f/S5vPxW68JrCedL6zSSL/vk5S8L9ldp81z8frStwJc0GviT/LT6w/CevHyy\ndpuI2Ez6hjcaOLBdsTWgbszZ8rw8tOQxtI3zpXtikDReUm8+Ieta0vzIGcDNEXFX60MdPOdLV8Xw\ne6RzNq7Iy8ck3S9pYmtDbJzzpTtjkHQs8D5S8Xl/i2JrmvOlK2J4MS8PKOhfeX/fU7DOsnYewb8K\neC9wd0TcU9U+Ni/X1dmu0r5HuwJrQDfE3A0xtJPzpXtiGA9cDlwGnEM6AvS3wKwWxtcs50vnY1gP\nfBX4bdIJceOAj5DO15gK3Cdpl5ZH2hjnS3fGcFZefrvpiFrL+dL5GP49L79SfXUiSb8BXJifFl59\nx5LR7dippPOBi0hH/k5vxxitJqm3oHl+RKwcovF7KCigIqJ3KMbvJOdLQ+P30KZ8iYilaQiNAvYD\nTgX+EviQpI9FxEvNjtEM50tD4/fQ4nyJiDWkL4HVHpA0g3TFjqOBM0kny3WM86Wh8Xto8/9HksYC\nnyJdKWZ+q/bbLOdLQ+P30Pp8uQw4ATgNWJIvoboL6cTgZ0nTjrbW39xaXuBLOpf0C/1nwPEFxUDl\nm9pYilXaX2l1bNtxeUFbH7CSoYm5p04MvXnZre9bU5wvDeupE0NvXjYdQ0RsIZ3odI2k1cAtpEL/\n3EHG2jLOl4b11ImhNy9bFkNEbJZ0A6nA/zAdLPCdLw3rqRNDb162IoZPk+ZdL4iIF/vpN2ScLw3r\nqRNDb14OOoaIeF7S7wBfBj4OfIE0beefST+j5aQrGlkdLS3wJc0B/oF0zdLj8xGeWsuASaS5Vv9Z\ns/1o0nyrzRSfWNE2EaF+Vi/Ly3pz1A7Jy3rzywYyfh/pbPeOxTDUnC/DKl8qJ2JNHWD/lnO+DKt8\nWZuXHZui43zp+nypnFx7/cAjax/nS/flS0SsJh1QestBJUmVE4IfGVSgI0zL5uBL+nPSh2MJ6XJL\n9b5ZLczLEwvWfZj0jX5xRLzRqthaYAXpSOahkopO+DgpLxcWrGuVyglIM2rvridpN2AKaU7sT9sY\nQ8s4X4DhlS/75eXmfnu1ifMFGF75UrkaxpAWOhXOF6CL80XS0cBvkU6u7WtjnAPifAG6OF8KVE6A\nvrk14ZVUK661SfoTSgCPAntup+/upKM7A75RRME++hjC68jm7Qd9o4ia7afS4RuL0CXXwXe+dGe+\nAEcBowr2sytwb97mCueL86UqX4pujHY8sDFvM9n54nwp2PY7uc9FQ50fzpfhkS+kA9C7FuzndNLc\n+4f6i9mPSLdgboakz5BOkNkCzKX4LOmVETG/apuZpFtAbwQWAC8BnyBd8ug20t0y3xKYpPlVT08E\n3gV8j3QbZYAbImJRVf/DgC9WbfMZ0jfEW6vaLo4Bzv3L17VdCEwm/SK4j3SSxx+QThKaHhEP12wz\nk3SbakjXdD2BdETrwdz2YkRcPJDx8/4OIv0S2Ru4k3T76KNJ15h9kvSf6a9qtvkicFh++n7SUZPF\nbLss1aKIuGGgMTTL+dK9+SLp+6QjKYvZdqfA/UlHePbI7SdExGsDjaFZzpeuzpc+0p/WFwOrcvOR\nbLue9pcj4q8GOn4rOF+6N1+qttsdeI40RXjCQF9zOzhfujdfJO0KrCYdXFpBKuqnAMfmbX83Ip4b\n6PgjUrPfENh2VLi/R1/BdlOAu4GXgQ3A/5AuffS2I4i5//bGmFXTf+oAtukZ5GvdmXSS4XLSN/i1\npA/cEQ2+NysbeL/3B+YBz5M+mE8DVwPj6vTv204M89vxzdH5MvzyBfgYcBPpl+060o1e1gA/Jl3O\nbvRgx3e+lDpfzgD+jXQi32s55mdIJ8EdN9S54nzp7nyp2uacPF7H71zrfOnefAHGkP7Ss4x0l9vX\nSVOoLgF27nTuDIdH00fwzczMzMyse7TzRldmZmZmZjbEXOCbmZmZmZWIC3wzMzMzsxJxgW9mZmZm\nViIu8M3MzMzMSsQFvpmZmZlZibjANzMzMzMrERf4ZmZmZmYl4gLfzMzMzKxEXOCbmZmZmZWIC3wz\nMzMzsxJxgW9mNsJIWilp5Ugd38ys7Fzgm5mNcJJmSQpJszodi5mZNc8FvpmZmZlZibjANzMzMzMr\nERf4ZmYlpORcSU9I2ijpWUnfkDS2pl8fMC8/nZen6lQePVX9Rkv6gqSfSvq1pPWS/juP8bb/SwY6\nflX/sZL+TNJCSaskbZK0VtK/Sjq2pu+4PP4KSaqzv7vya5g0qDfOzKwEFBGdjsHMzFpM0jXA+cDz\nwG3Am8ApwMvAfsCmiOjJ8+5n5nV3AkuqdnN1RLwiaQxwF3ACsAzoAzYC04AjgZsi4vRGxq/qfwzw\nQH6syP0mAp8AdgJOjogfVvW/EZgNzIiIe2vG3h94ClgSES7wzWwMVTl8AAADxElEQVTEcYFvZlYy\nkiYDD5EK5Q9GxEu5/R3A/cAxwNOVAjsX+fOA2RExv2B/vcDlwDeAORGxJbePAr4FfBaYGRF3NjJ+\nXjcWGBMRL9aMPQH4D2BdRBxe1T4JeAS4PSJOqxPvWRHx7QG/cWZmJeEpOmZm5TM7L6+oFNcAEbER\n+NJgdpSn35wHvABcWCnu8/62ABcBAfxxM+NHxLra4j63ryL9BeAwSROr2h8FHgVOkbRPVbyjgDOA\nV4FbBvNazczKYnSnAzAzs5Y7Ki9/UrBuEbCloL2eQ4E9geXAX9SZ8r4BOLzqeUPjS5oCXAAcC+wN\n7FjTZT/gmarn1wI3kv6C8LXc9lFgAvDNiHit8BWZmZWcC3wzs/KpnMi6unZFRGyW9LYj5f3YKy8P\nIU17qWfXZsaXdCrpSP1G4F7S9J7Xga3AVOAjpLn41RYAfwd8TtJVEbEVOCuvu76fWM3MSs0FvplZ\n+azLy3cBv6xeIWk0MB5YNch93RERv9/G8b8KbAImRcTPa7a5nlTgv0VEbJA0H7gQmCHpCeAk4OGI\neGyAsZqZlY7n4JuZlc9/5eXbimLgQ8ComrbKlJnadoClwCvAMflqOu0YH+Bg4GcFxf0OeZt6vkk6\nB+Bs0tz7UfjovZmNcC7wzczKZ35eXippz0pjvorNlQX9f5WXE2tXRMRmYC6wL/B1Se+s7SNpX0lH\nNDE+wErgEEnvruovoBc4os42RMRy4D7g48DnSV9GFtTrb2Y2EvgymWZmJSTp66Sr32z3OvSSxpGm\nzGwGvku6Yg7A3IhYl4/c30a6Jv2zwMK83Js0N38KcGlEXNXI+Ln/2cB1wBrg9tx/Cqm4/zFwMjAt\nIvoKXuupwPeqYj5/8O+YmVl5uMA3MyuhfPT7T/PjQNJR+juAS4DHAGoK7BNJJ9G+D9glNx8QESur\n9vdpYBbwAdJJtWtJN5S6G/huRPxfo+PnbWYBc0hfGjYADwKXAZ/MsdUr8EeRvpSMB94bEU8M+I0y\nMyshF/hmZjasSToQ+AXwUEQc1+l4zMw6zXPwzcxsuLsYEOlOu2ZmI56P4JuZ2bCT72r7R6TpPLOB\nx4Gj8rXwzcxGNF8H38zMhqMDSVfkWU+6MdY5Lu7NzBIfwTczMzMzKxHPwTczMzMzKxEX+GZmZmZm\nJeIC38zMzMysRFzgm5mZmZmViAt8MzMzM7MScYFvZmZmZlYiLvDNzMzMzErEBb6ZmZmZWYm4wDcz\nMzMzKxEX+GZmZmZmJeIC38zMzMysRFzgm5mZmZmViAt8MzMzM7MS+X/IzNoeXik6hgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd735d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 虚拟变量（哑变量）\n",
    "\n",
    "下面是一些分类变量，例如季节、天气、月份。要在我们的模型中包含这些数据，我们需要创建二进制虚拟变量。用 Pandas 库中的 `get_dummies()` 就可以轻松实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 调整目标变量\n",
    "\n",
    "为了更轻松地训练网络，我们将对每个连续变量标准化，即转换和调整变量，使它们的均值为 0，标准差为 1。\n",
    "\n",
    "我们会保存换算因子，以便当我们使用网络进行预测时可以还原数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 将数据拆分为训练、测试和验证数据集\n",
    "\n",
    "我们将大约最后 21 天的数据保存为测试数据集，这些数据集会在训练完网络后使用。我们将使用该数据集进行预测，并与实际的骑行人数进行对比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的，所以我们用历史数据进行训练，然后尝试预测未来数据（验证数据集）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 开始构建网络\n",
    "\n",
    "下面你将构建自己的网络。我们已经构建好结构和反向传递部分。你将实现网络的前向传递部分。还需要设置超参数：学习速率、隐藏单元的数量，以及训练传递数量。\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "该网络有两个层级，一个隐藏层和一个输出层。隐藏层级将使用 S 型函数作为激活函数。输出层只有一个节点，用于递归，节点的输出和节点的输入相同。即激活函数是 $f(x)=x$。这种函数获得输入信号，并生成输出信号，但是会考虑阈值，称为激活函数。我们完成网络的每个层级，并计算每个神经元的输出。一个层级的所有输出变成下一层级神经元的输入。这一流程叫做前向传播（forward propagation）。\n",
    "\n",
    "我们在神经网络中使用权重将信号从输入层传播到输出层。我们还使用权重将错误从输出层传播回网络，以便更新权重。这叫做反向传播（backpropagation）。\n",
    "\n",
    "> **提示**：你需要为反向传播实现计算输出激活函数 ($f(x) = x$) 的导数。如果你不熟悉微积分，其实该函数就等同于等式 $y = x$。该等式的斜率是多少？也就是导数 $f(x)$。\n",
    "\n",
    "\n",
    "你需要完成以下任务：\n",
    "\n",
    "1. 实现 S 型激活函数。将 `__init__` 中的 `self.activation_function`  设为你的 S 型函数。\n",
    "2. 在 `train` 方法中实现前向传递。\n",
    "3. 在 `train` 方法中实现反向传播算法，包括计算输出错误。\n",
    "4. 在 `run` 方法中实现前向传递。\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
    "                                       (self.input_nodes, self.hidden_nodes))\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.output_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        #### TODO: Set self.activation_function to your implemented sigmoid function ####\n",
    "        #\n",
    "        # Note: in Python, you can define a function with a lambda expression,\n",
    "        # as shown below.\n",
    "        self.activation_function = lambda x : 1 / (1 + np.exp(-x))  # Replace 0 with your sigmoid calculation.\n",
    "        \n",
    "        ### If the lambda code above is not something you're familiar with,\n",
    "        # You can uncomment out the following three lines and put your \n",
    "        # implementation there instead.\n",
    "        #\n",
    "        #def sigmoid(x):\n",
    "        #    return 0  # Replace 0 with your sigmoid calculation here\n",
    "        #self.activation_function = sigmoid\n",
    "                    \n",
    "    \n",
    "    def train(self, features, targets):\n",
    "        ''' Train the network on batch of features and targets. \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            \n",
    "            features: 2D array, each row is one data record, each column is a feature\n",
    "            targets: 1D array of target values\n",
    "        \n",
    "        '''\n",
    "        n_records = features.shape[0]\n",
    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
    "        \n",
    "        for X, y in zip(features, targets):\n",
    "            #### Implement the forward pass here ####\n",
    "            ### Forward pass ###\n",
    "            # TODO: Hidden layer - Replace these values with your calculations.\n",
    "            hidden_inputs = np.dot(X,self.weights_input_to_hidden) #None # signals into hidden layer\n",
    "            hidden_outputs = self.activation_function(hidden_inputs) #None # signals from hidden layer\n",
    "\n",
    "            # TODO: Output layer - Replace these values with your calculations.\n",
    "            final_inputs = np.dot(hidden_outputs,self.weights_hidden_to_output) #None # signals into final output layer\n",
    "            final_outputs = final_inputs #None # signals from final output layer\n",
    "    \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "\n",
    "            # TODO: Output error - Replace this value with your calculations.\n",
    "            error = y-final_outputs #None # Output layer error is the difference between desired target and actual output.\n",
    "            output_error_term = error# * final_outputs*(1-final_outputs)\n",
    "       \n",
    "            # TODO: Calculate the hidden layer's contribution to the error\n",
    "            hidden_error = np.dot(error[:,None],self.weights_hidden_to_output.T) #None\n",
    "            #hidden_error = np.dot(output_error_term[:,None],self.weights_hidden_to_output.T)\n",
    "            \n",
    "            # TODO: Backpropagated error terms - Replace these values with your calculations.\n",
    "            \n",
    "            hidden_error_term = hidden_error*hidden_outputs*(1-hidden_outputs) #None\n",
    "\n",
    "            # Weight step (input to hidden)\n",
    "            delta_weights_i_h += self.lr*np.dot(X[:,None],hidden_error_term) #None\n",
    "            # Weight step (hidden to output)\n",
    "            delta_weights_h_o += self.lr*np.dot(hidden_outputs[:,None],error[:,None]) #None\n",
    "\n",
    "\n",
    "        # TODO: Update the weights - Replace these values with your calculations.\n",
    "        self.weights_hidden_to_output += delta_weights_h_o/n_records #None # update hidden-to-output weights with gradient descent step\n",
    "        self.weights_input_to_hidden += delta_weights_i_h/n_records #None # update input-to-hidden weights with gradient descent step\n",
    " \n",
    "    def run(self, features):\n",
    "        ''' Run a forward pass through the network with input features \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            features: 1D array of feature values\n",
    "        '''\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        # TODO: Hidden layer - replace these values with the appropriate calculations.\n",
    "        hidden_inputs = np.dot(features,self.weights_input_to_hidden) #None # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs) #None # signals from hidden layer\n",
    "        \n",
    "        # TODO: Output layer - Replace these values with the appropriate calculations.\n",
    "        final_inputs = np.dot(hidden_outputs,self.weights_hidden_to_output) #None # signals into final output layer\n",
    "        final_outputs = final_inputs #None # signals from final output layer \n",
    "\n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 单元测试\n",
    "\n",
    "运行这些单元测试，检查你的网络实现是否正确。这样可以帮助你确保网络已正确实现，然后再开始训练网络。这些测试必须成功才能通过此项目。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.005s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    def runTest(self):\n",
    "        test_upper (self)\n",
    "        test_isupper (self)\n",
    "        test_split (self)\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 训练网络\n",
    "\n",
    "现在你将设置网络的超参数。策略是设置的超参数使训练集上的错误很小但是数据不会过拟合。如果网络训练时间太长，或者有太多的隐藏节点，可能就会过于针对特定训练集，无法泛化到验证数据集。即当训练集的损失降低时，验证集的损失将开始增大。\n",
    "\n",
    "你还将采用随机梯度下降 (SGD) 方法训练网络。对于每次训练，都获取随机样本数据，而不是整个数据集。与普通梯度下降相比，训练次数要更多，但是每次时间更短。这样的话，网络训练效率更高。稍后你将详细了解 SGD。\n",
    "\n",
    "\n",
    "### 选择迭代次数\n",
    "\n",
    "也就是训练网络时从训练数据中抽样的批次数量。迭代次数越多，模型就与数据越拟合。但是，如果迭代次数太多，模型就无法很好地泛化到其他数据，这叫做过拟合。你需要选择一个使训练损失很低并且验证损失保持中等水平的数字。当你开始过拟合时，你会发现训练损失继续下降，但是验证损失开始上升。\n",
    "\n",
    "### 选择学习速率\n",
    "\n",
    "速率可以调整权重更新幅度。如果速率太大，权重就会太大，导致网络无法与数据相拟合。建议从 0.1 开始。如果网络在与数据拟合时遇到问题，尝试降低学习速率。注意，学习速率越低，权重更新的步长就越小，神经网络收敛的时间就越长。\n",
    "\n",
    "\n",
    "### 选择隐藏节点数量\n",
    "\n",
    "隐藏节点越多，模型的预测结果就越准确。尝试不同的隐藏节点的数量，看看对性能有何影响。你可以查看损失字典，寻找网络性能指标。如果隐藏单元的数量太少，那么模型就没有足够的空间进行学习，如果太多，则学习方向就有太多的选择。选择隐藏单元数量的技巧在于找到合适的平衡点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.059 ... Validation loss: 0.144"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 5000\n",
    "learning_rate = 0.7\n",
    "hidden_nodes = 20\n",
    "output_nodes = 1\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M65RLkiSpSOX8oGfhI8+/XWeOeGGF+PuaOeYhYAUwNCI6lLE2SZIkaaNRlgk1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\nERERkZIpKRcRERERKZmSchERERGRkv0/Mu3GLRvPNbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf482d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 检查预测结果\n",
    "\n",
    "使用测试数据看看网络对数据建模的效果如何。如果完全错了，请确保网络中的每步都正确实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qsal/R+vf2XaFQXsesay0j1Q89A6aOaePS8IJ/f3PrcebtdrOH9/3gq1RhXB8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+e099hOpoFhug9kAyCTqSvTPofoYaaV/bLytZyHSCjTXgeQyPFWsrCBSvNjnvi/GerRXm3c40NR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UTSsatGezZHD/aR4sIIS8JeZtcvRB5KLKNGZUPOlzpJxVv2TfEE3shSuMOb97whPFCHu+WQ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\nHBeyTiikGGmMt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2nrLEEod4zntxXljlJKQbDLnbhDUMsd1607/qwOctmjlCpwXwau3AHv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\nSKNdEARBEARBEARBEALK/w//rNJZOBg7DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0e03d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 可选：思考下你的结果（我们不会评估这道题的答案）\n",
    "\n",
    " \n",
    "请针对你的结果回答以下问题。模型对数据的预测效果如何？哪里出现问题了？为何出现问题呢？\n",
    "\n",
    "> **注意**：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter\n",
    "\n",
    "#### 请将你的答案填写在下方\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
